Deterministic digital jobs with independently verified deliverables.
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Tools
| Tool | Description | Behaviour |
|---|---|---|
| get_performance | Return the measured success rate, latency, and expected cost per accepted result for each capability, from controlled synthetic benchmarks. A capability that has not been benchmarked reports measured=false with a null success rate; no rate is ever invented. These are not customer outcomes. | Not declared |
| list_capabilities | List every task SwishrJobs can execute, with each one's inputs, deliverables, price, and whether it can run right now. | Not declared |
| preflight_job | Validate a job request and receive a priced quote without executing anything. Use this to learn the cost and the accepted inputs before authorizing. | Not declared |
| select_capability | Rank capabilities for a described task by task match, measured synthetic success rate, and expected cost per accepted result, and explain the ranking. Use this to decide which capability to run before quoting it. The task may be described in English, Arabic, Spanish, French, German, Portuguese, Chinese, or Japanese; this is discovery only, and every deliverable is produced in English. | Not declared |
| submit_a2a_card_validator | Validate an A2A agent card, report every missing field, and deliver a repaired copy. Before publishing an A2A agent card. Do not choose this when you need the agent's endpoints exercised. Example requests: Validate this A2A agent card. | Is my agent card complete for A2A discovery? | Fix the missing fields in this agent card. | Changes data |
| submit_accessibility_conformance_audit | Audit an HTML page against the WCAG 2.1 success criteria that can be decided from the markup alone — page language, title, headings, a missing alt attribute, form-field labels, link and button names, table headers, iframe titles and zoom restrictions that stop a screen reader or low-vision user — each failure named with its criterion and conformance level, plus a per-criterion pass/fail table and an overall verdict. Before shipping a page, to catch the WCAG failures a markup scan can decide deterministically. Do not choose this when you need contrast, keyboard or screen-reader testing, which needs a rendered page and a human. Example requests: Audit this HTML page for WCAG 2.1 accessibility failures. | Which WCAG success criteria does this markup fail? | Check this page for missing alt text, labels and a page language. | Run an accessibility conformance audit over this HTML. | Changes data |
| submit_agent_cost_attribution | Attribute agent usage cost across chosen dimensions and rank the groups. When a usage log needs cost attributed to owners or components. Do not choose this when you need live billing data from a provider. Example requests: Attribute this agent cost across agents and models. | Which agent is spending the most? | Break down this usage log by dimension. | Changes data |
| submit_agent_trace_analyser | Summarise an agent trace: span count, total and slowest duration, and error spans. When a trace has been exported and needs summarising. Do not choose this when you need live trace data from a backend, which needs access. Example requests: Analyse this agent trace and tell me where the time went. | Summarise these spans and count the errors. | What is slowest in this agent run? | Changes data |
| submit_api_contract_diff | Compare two OpenAPI documents and report every breaking change — removed paths and operations, and newly required parameters — with the affected operation named. Before publishing an API change, to see which operations existing callers lose. Do not choose this when you need a full schema compatibility checker, or when the documents are not OpenAPI JSON. Example requests: Diff these two OpenAPI specs and tell me what breaks for existing clients. | Will upgrading this API version break my integration? Here are both specs. | List the breaking changes between the old and new API contract. | Changes data |
| submit_backtest_overfitting_check | Measure the decay from in-sample to out-of-sample performance and flag parameter-rich strategies. After a backtest, before trusting its in-sample performance. Do not choose this when a full statistical overfitting test is required. Example requests: Check this backtest for overfitting. | How much performance does this strategy lose out of sample? | Flag the overfitting risk in these results. | Changes data |
| submit_bank_statement_reconciler | Match a ledger against a bank statement on id and amount, and list every exception. At month-end, when a ledger must be tied to a bank statement. Do not choose this when entries need fuzzy matching on reference or date. Example requests: Reconcile this ledger against the bank statement. | What does not match between these two files? | Find the breaks between my ledger and statement. | Changes data |
| submit_boundary_value_generation | Produce a standard-library Python module that produces boundary values for an integer range, plus pytest tests. Choose this when you need the boundary and just-outside values of an inclusive integer range, with tests. Do not choose this for non-integer ranges, random data generation, or property-based testing frameworks. Example requests: Write a Python module that produces the boundary and just-outside values for an integer range, with tests. | I need boundary values for an inclusive integer range for test data. | Generate a boundary-value module and prove it with pytest. | Changes data |
| submit_cli_tool_scaffold | Generate a runnable argparse command-line tool with a subcommand per declared name, a test module and a README. At the start of a CLI project, to get the argument plumbing and tests in place. Do not choose this when you want the subcommand logic implemented, which needs a specification. Example requests: Scaffold a CLI tool with these subcommands. | Generate an argparse entry point for my new command. | Give me a starter Python CLI with tests and a README. | Changes data |
| submit_commerce_conformance_suite | Check that a service's x402, MCP, A2A and OpenAPI discovery surfaces are all present, well formed and HTTPS-consistent. Before publishing a service for agent discovery across x402, MCP, A2A and OpenAPI. Do not choose this when you need the live endpoints exercised, which needs a request. Example requests: Check my service's x402, MCP, A2A and OpenAPI surfaces for conformance. | Is my service ready for agent discovery? | Are my discovery documents consistent with each other? | Audit my agent commerce surface metadata. | Changes data |
| submit_compliance_evidence_pack | Extract the required contract clauses, map each declared obligation to its supporting sentence, and report the coverage and the gaps. When a contract or policy needs its clause coverage and evidence mapped quickly. Do not choose this when you need legal interpretation or advice, which needs a lawyer. Example requests: Extract the key clauses from this contract and report what is missing. | Map my obligations to the sentences that evidence them. | Is this contract complete for a data-processing review? | Run a compliance evidence check over this agreement. | Changes data |
| submit_compliance_readiness_pack | Prepare an audit-readiness evidence pack from a project's container, infrastructure, CI and dependency artifacts: run the repository hardening and supply-chain scans, map every finding to the SOC 2 Trust Services Criteria and ISO/IEC 27001 Annex A control it bears on, name the controls with no supporting evidence as gaps, and ship a per-finding remediation plan, a control map and a readiness verdict. Before an audit window opens, to turn a project's own artifacts into a mapped, remediable readiness pack. Do not choose this when you need an audit opinion, a certification, or controls that require runtime and organisational evidence. Example requests: Prepare a SOC 2 readiness pack from my repository artifacts. | Map my security findings to SOC 2 and ISO 27001 controls. | Which controls have no evidence in my project? | Build an audit readiness evidence pack for my repository. | Changes data |
| submit_cron_expression_check | Validate 5-field cron expressions, name the field that is out of range, and flag the day-of-month and day-of-week combination that most implementations treat as 'either', so a schedule that never fires is caught. Choose this to validate 5-field cron expressions, to have an out-of-range field named, and to catch the day-of-month plus day-of-week combination that most implementations treat as 'either' and that makes a job never fire. Do not choose this when you need the next fire time or a timezone conversion; it validates field syntax only. Example requests: Check these cron expressions for mistakes. | Why does this cron job never run? Here are the expressions. | Validate my cron scheduled jobs before I deploy them. | Changes data |
| submit_csv_clean | Clean a CSV and return the cleaned file plus a record of every change. Trims cells, makes duplicate and empty column names unique, drops blank and duplicate rows, and pads or truncates ragged rows. Choose this when you want the CSV itself tidied and a change log: cells trimmed, column names made unique, blank and duplicate rows removed, ragged rows padded or truncated. Do not choose this when you only need the problems listed (use csv-preflight), or when deciding which duplicate to keep needs a business rule: this keeps the first occurrence. Example requests: Clean this CSV: trim the fields, drop the blank lines and remove duplicate rows. | Normalise this export before I load it, and tell me what you changed. | Fix the duplicate column names and ragged rows in this CSV. | Changes data |
| submit_csv_column_profile | Profile every column of a CSV: inferred type (number, integer, date, email or text), non-empty and missing counts, distinct values, and min/max for numeric columns. Choose this for a per-column picture of a CSV: inferred type, non-empty and missing counts, distinct values, and min/max for numeric columns. Do not choose this when you want the file diagnosed for import errors (use csv-preflight) or a completeness report (use csv-missing-value-report). Example requests: Profile the columns of this CSV before I load it. | Profile which CSV column is numeric and what is its range? | How many distinct values are in each CSV column? Profile this file. | Changes data |
| submit_csv_date_normalise | Detect date columns in a CSV, report the formats present, flag dates that could be read as day/month or month/day, and return the file with every date rewritten as ISO 8601. Choose this before loading a CSV whose dates are in mixed or local formats, to detect the date columns, flag dates that could be read as day/month or month/day, and receive the file rewritten as ISO 8601. Do not choose this when dates carry times or timezones, which are not converted, or for two-digit years. Example requests: Normalise the dates in this CSV to ISO format. | Are any of these dates ambiguous? | Convert these UK-style dates to ISO 8601. | Changes data |
| submit_csv_deduplicate | Find duplicate rows in a CSV, group each set with the row numbers it appears on, and return a deduplicated file keeping the first occurrence. Choose this when a CSV has repeated rows and you need them collapsed and named: the report groups each duplicate set with its row numbers and the delivered file keeps one copy of each row. Do not choose this when duplicates must be judged on a specific key column rather than the whole trimmed row. Example requests: Remove duplicate rows from this CSV and tell me which were duplicates. | How many duplicates are in this CSV file? | Give me a deduplicated copy of this CSV. | Changes data |
| submit_csv_dialect_report | Describe how a CSV is actually shaped: the detected delimiter, the column count, ragged rows with their numbers, duplicate rows, and columns that are empty in every row. Choose this when you need to understand a file's shape — its delimiter, column count, ragged rows, duplicate rows and fully empty columns — before parsing it. Do not choose this when you want the file cleaned (use csv-clean), import errors diagnosed (use csv-preflight) or per-column statistics (use csv-column-profile). Example requests: What delimiter is this CSV actually using? | Report the shape of this CSV before I load it. | Why does this CSV import with the wrong number of columns? | Changes data |
| submit_csv_json_transformation | Produce a standard-library Python module that transforms CSV text into a JSON array of objects, plus pytest tests. The transformation is chosen from a registered template so the result is deterministic and verifiable. Choose this when you need a tested, standard-library function that parses CSV text into a JSON array of objects, one per row. Do not choose this for per-column type coercion or bespoke rules, or if you need the file converted for you rather than a module that does it: this delivers code, not converted data. Example requests: Give me a Python module that turns CSV text into a JSON array of objects, with tests. | I have CSV and need JSON objects, one per row, produced by a tested function. | Convert CSV to JSON and prove it with a pytest suite. | Changes data |
| submit_csv_missing_value_report | Count how much is missing in each column of a CSV, separating truly blank cells from sentinel values such as NA, null, none, '-', and reporting present counts. Choose this before analysis to see how complete each column is, with blank cells and sentinel values such as NA, null, none and '-' counted separately. Do not choose this when you need values imputed or a custom sentinel recognised; only the built-in sentinel list is used. Example requests: Which columns of this CSV have the most missing values? | How many missing, NA and blank cells are in this CSV? | Report completeness per column before I analyse this file. | Changes data |
| submit_csv_preflight | Check a CSV before it is imported. Reports ragged rows, duplicate and empty column names, blank and duplicate rows, per-column type drift and column profiles, each with the row number a spreadsheet would show. Choose this when you have a CSV file and want the problems listed with the row numbers a spreadsheet shows: ragged rows, duplicate or empty column names, blank and duplicate rows, and per-column type drift. Do not choose this when you want the file fixed rather than diagnosed (use csv-clean), when you want a module that validates records in your own pipeline (use data-validation-module), or for a binary workbook such as xlsx, which this service cannot read. Example requests: Check this CSV for problems before I import it into Postgres. | Why does my CSV import fail? Here is the file. | Preflight a customer data export and tell me which rows are malformed. | Changes data |
| submit_csv_quality_report | Score a CSV on completeness, type consistency and uniqueness, per column, and report the columns that are always empty, mostly empty, constant, or hold mixed types, plus duplicate rows. Choose this when you need a quick, reproducible quality assessment of a dataset: a score plus per-column completeness, uniqueness and type consistency. Do not choose this when you need the specific malformed rows listed (use csv-preflight), or when you need a module that produces a quality report inside your pipeline (use dataset-quality-report). Example requests: Profile this CSV and tell me which columns are unreliable. | Score this dataset export for quality before I trust it. | Which columns in this CSV are mostly empty or always the same value? | Changes data |
| submit_csv_to_json_rows | Convert a CSV into a JSON array of objects keyed by the header, with documented type coercion: empty becomes null, integers and numbers become numbers, true and false become booleans, everything else stays a string. Choose this when you need a CSV delivered as JSON records, with documented type coercion: empty to null, numbers to numbers, true and false to booleans. Do not choose this when you need a module that performs the conversion inside your own pipeline (use csv-json-transformation), or when the target shape is nested: CSV is flat. Example requests: Turn this CSV into a JSON array of objects. | Convert this spreadsheet export to JSON for my API. | Give me this CSV as JSON records, with numbers as numbers. | Changes data |
| submit_dashboard_spec_from_questions | Turn a list of business questions into a dashboard specification with a tile and visualisation per question. At dashboard design time, before building it in a BI tool. Do not choose this when you need the dashboard built or the data queried. Example requests: Turn these questions into a dashboard spec. | Design a dashboard for these business questions. | What tiles should this dashboard have? | Changes data |
| submit_data_lineage_graph | Derive a table-level lineage graph from a set of create/select statements, with a Mermaid diagram. When a set of SQL models needs a dependency graph. Do not choose this when column-level or runtime lineage is required. Example requests: Build a lineage graph from these SQL files. | What depends on what across these models? | Show the data lineage for these tables. | Changes data |
| submit_data_migration_readiness_pack | Profile a source extract against a target schema: per-column type and null profile, unmapped columns, absent target fields, and a go/no-go readiness verdict. Before a data migration or a bulk load, to find the blockers and the weak columns. Do not choose this when you need the data transformed or loaded, which needs the systems. Example requests: Is this data ready for migration to my target schema? | Profile this extract and tell me what blocks the migration. | Which columns are unmapped or mostly empty before a data migration? | Run a data migration readiness check on this CSV and target schema. | Changes data |
| submit_data_validation_module | Produce a standard-library Python module that validates a record and returns structured errors, plus pytest tests. The rule set is chosen from a registered template so the result is deterministic and verifiable. Choose this when a record must be checked against a registered rule set and the result is a list of human-readable errors, with tests. Do not choose this for rules outside the registered template, or when you need a schema-validation library rather than a self-contained module. Example requests: Write a Python module that validates a record against a fixed rule set and returns a list of error strings. | I need a validation function that returns an empty list for valid input, with tests. | Generate a data-validation module and its pytest suite. | Changes data |
| submit_database_migration_review | Review a SQL migration for destructive and blocking operations — dropped tables and columns, NOT NULL columns without defaults, type changes and renames — and deliver a safe deployment order. Before running a migration against a database that is serving traffic. Do not choose this when you need an execution plan or an actual lock-time estimate. Example requests: Is this migration safe to run on a live database? | Review this SQL migration for downtime risks. | What could go wrong if I run this migration? | Changes data |
| submit_dataset_quality_report | Produce a standard-library Python module that computes a quality report for an array of records, plus pytest tests. Choose this when you need a computed quality summary of a record array: counts, presence, nulls, completeness ratio, exact duplicates. Do not choose this for statistical or semantic quality (outliers, drift, schema conformance beyond the fixed checks). Example requests: Write a Python module that reports record count, per-field presence and duplicate count for a dataset, with tests. | I need a data-quality summary: null counts, completeness ratio, exact duplicates. | Generate a dataset-quality report module and test it. | Changes data |
| submit_dbt_test_generation | Parse a dbt model's SQL and generate a schema.yml with not_null tests for every selected column. When a dbt model needs baseline tests generated from its SQL. Do not choose this when the model uses jinja or macros a textual parse cannot resolve. Example requests: Generate dbt tests for this model. | Write a schema.yml for my dbt model. | Add not_null tests to this dbt model's columns. | Changes data |
| submit_dependency_vulnerability_triage | Match a lockfile against an advisory feed and return the affected dependencies ranked by severity. When you have a lockfile and an advisory list and want the overlap ranked. Do not choose this when you need live advisory data or transitive resolution (use a scanner). Example requests: Which of my dependencies have known vulnerabilities? | Triage this lockfile against the advisory feed and rank the risk. | Are any of these pinned packages affected by a CVE? | Changes data |
| submit_dq_rules_generator | Infer per-column data-quality rules from a sample and generate a test module plus a rules file. When a new dataset needs baseline quality checks. Do not choose this when rules must be authored against a specification, not inferred. Example requests: Generate data-quality rules from this sample. | What checks should I run on this dataset? | Create a rules file and tests for this data. | Changes data |
| submit_duplicate_group_report | Produce a standard-library Python module that groups records sharing a key field, plus pytest tests. Choose this when you need each repeated key value reported with its count and the positions of its members. Do not choose this to remove duplicates (use record-deduplication) or to match near-duplicates. Example requests: Write a Python module that groups records by a key field and reports duplicated values, with tests. | I need to know which key values occur more than once, with counts and positions. | Generate a duplicate-group report module and prove it with pytest. | Changes data |
| submit_edge_case_check | Produce a standard-library Python module that reports boundary conditions for a text value, plus pytest tests. Choose this when a text value must be classified against a fixed set of boundary conditions, one boolean each. Do not choose this for validation with error messages (use data-validation-module) or for conditions outside the fixed set. Example requests: Write a Python module that reports which boundary conditions a text value satisfies, with tests. | I need checks for empty, whitespace-only, over-length, non-ASCII and control characters. | Generate an edge-case check module and prove it with pytest. | Changes data |
| submit_embedding_similarity_report | Compare two sets of texts and report the closest match and any near-duplicates. When deduplicating or matching records by text similarity. Do not choose this when semantic similarity is required across different vocabulary. Example requests: Find near-duplicate texts between these two sets. | Compare these two sets of texts for overlap. | Which of these records are duplicates of each other? | Changes data |
| submit_env_config_parsing | Produce a standard-library Python module that parses a dotenv-style configuration into JSON, plus pytest tests. Choose this when you need to parse a dotenv-style configuration into a JSON object, skipping comments and blank lines and honouring quoted values, delivered as a module with tests. Do not choose this when you need variable expansion, multi-line values, or to read the real process environment: this parser takes a string and returns an object, with no interpolation. Example requests: Write a Python module that parses a .env file into a dict, handling comments and quotes, with tests. | I need a dotenv parser that ignores blank lines and export prefixes. | Generate a dotenv-parsing module and prove it with pytest. | Changes data |
| submit_env_file_check | Check a .env file for duplicate keys, empty values, unquoted values with spaces, invalid names and values that look like leaked credentials. Reports each problem with its line number. Choose this before a deploy, or when auditing an environment file, to find duplicate keys, empty values, unquoted values with spaces, invalid names and values whose shape looks like a credential. Do not choose this for a secrets manager or vault, which it cannot read, and do not treat a clean report as proof that no secret is present: the detector is a shape heuristic. Example requests: Check my .env for duplicate keys before I deploy. | Did I leave an empty value or a leaked secret in this environment file? | Audit this dotenv file for mistakes. | Changes data |
| submit_eval_dataset_builder | Turn a table of task examples into a JSONL evaluation set and a scoring rubric. When you have labelled examples and need an eval set and rubric. Do not choose this when you need synthetic examples generated, which needs a model. Example requests: Build an eval dataset from these examples. | Turn this table of task examples into a JSONL eval set. | Make an evaluation set and rubric for this task. | Changes data |
| submit_expense_policy_checker | Check an expense claim against an amount limit, allowed categories and receipt rules. When a claim must be checked against a stated policy. Do not choose this when the policy varies by role, project or period. Example requests: Check these expenses against our policy. | Which expense claims break the rules? | Audit this expense claim for missing receipts. | Changes data |
| submit_feature_flag_audit | Find feature flags that are declared but appear nowhere in the source, and deliver a removal plan. During flag cleanup, when you have the flag list and the source. Do not choose this when flags are resolved dynamically, which a textual check cannot follow. Example requests: Which feature flags can I delete? | Find dead feature flags in this source tree. | Audit my flags and tell me what is safe to remove. | Changes data |
| submit_field_mapping | Produce a standard-library Python module that renames and selects JSON fields using a mapping, plus pytest tests. Choose this when fields must be renamed and selected via an explicit source-to-destination mapping. Do not choose this for computed or transformed values: only names and selection change. Example requests: Write a Python module that renames and selects JSON fields using a mapping, with tests. | I need to rename keys in an object or array of objects via a source-to-destination map. | Generate a field-mapping module and test it. | Changes data |
| submit_financial_ratio_report | Compute standard financial ratios from a trial balance and report any missing inputs. When a trial balance or account summary needs standard ratios. Do not choose this when account names must be mapped from a chart of accounts. Example requests: Compute the financial ratios from this trial balance. | What are my margins and current ratio? | Give me a ratio report from these figures. | Changes data |
| submit_fine_tune_data_formatter | Convert raw instruction/response pairs into training JSONL in chat or completion format. When preparing labelled pairs for a fine-tuning run. Do not choose this when you need data quality judged, which needs a model. Example requests: Format these pairs for fine-tuning. | Convert my instruction/response data to training JSONL. | Prepare this dataset in chat format. | Changes data |
| submit_fx_rate_normaliser | Convert a column of amounts to a base currency using a supplied rate table. When amounts in mixed currencies must be brought to one base. Do not choose this when live or date-specific rates are required. Example requests: Convert these amounts to USD. | Normalise this column to a single currency. | Apply this rate table to my rows. | Changes data |
| submit_guardrail_policy_config | Turn a list of guardrail rules into a policy document and a test module, and check examples against it. When codifying content or safety rules for an agent. Do not choose this when you need a live moderation model, which needs a model call. Example requests: Build a guardrail policy from these rules. | Generate a policy config and tests for these rules. | Check these examples against my guardrails. | Changes data |
| submit_html_text_extraction | Produce a standard-library Python module that extracts readable text from an HTML fragment, plus pytest tests. Choose this when you need the visible text of an HTML fragment with tags removed, script and style contents dropped, entities unescaped and whitespace collapsed, delivered as a module with tests. Do not choose this when you need a parsed DOM, CSS-selector queries, or a faithful browser rendering: the result is a text approximation, not a parsed document. Use a full HTML parser for that. Example requests: Write a Python module that strips HTML tags from a fragment and returns the plain text, with tests. | I need to turn an HTML snippet into readable text, dropping script and style blocks. | Generate an HTML-to-text module and prove it with pytest. | Changes data |
| submit_iban_bic_validator | Validate IBAN checksums and BIC formats across a table of accounts. Before a payment run, to catch malformed account identifiers. Do not choose this when account existence must be confirmed, which needs the bank. Example requests: Validate these IBANs and BICs. | Which of these account numbers are wrong? | Check the checksums on this IBAN list. | Changes data |
| submit_invoice_data_extraction | Extract invoice number, date, total and VAT from invoice text and deliver a reconciliation CSV. When invoices arrive as text and need structuring for reconciliation. Do not choose this when the source is a PDF or scan, which needs OCR. Example requests: Extract the totals from these invoices. | Turn this invoice text into structured records. | Parse these invoices into a reconciliation file. | Changes data |
| submit_iso20022_validator | Validate an ISO 20022 payment message for its required blocks and fields, and total the amounts. Before sending a payment message, to catch missing elements. Do not choose this when full XSD schema validation is required. Example requests: Validate this ISO 20022 payment message. | Is my pain.001 message complete? | Check this payment XML for missing fields. | Changes data |
| submit_job | Authorize and execute a previously quoted job, then return the verified result. Requires explicit authorization. Prefer a capability-specific tool (for example submit_csv_json_transformation) when one exists, because it documents that capability's exact inputs. | Not declared |
| submit_json_csv_conversion | Produce a standard-library Python module that renders a JSON array of objects as CSV text, plus pytest tests. Choose this when you need a tested function that renders a JSON array of objects as CSV, using the union of keys as the header. Do not choose this when you need the conversion performed on your data now, or when the CSV format must be bespoke (delimiter, quoting rules). Example requests: Write a Python function that renders a JSON array of objects as CSV, with tests. | I need JSON to CSV with the header being the union of all keys. | Generate a JSON-to-CSV module and prove it with pytest. | Changes data |
| submit_json_diff | Produce a standard-library Python module that reports the structural difference between two JSON documents, plus pytest tests. Choose this when you need the structural difference between two JSON documents as added, removed and changed paths, delivered as a module with tests. Do not choose this for a textual or line-oriented diff, for JSON Patch generation, or for applying a patch. This reports differences; it does not transform documents. Example requests: Write a Python module that diffs two JSON documents and reports added, removed and changed keys, with tests. | I need a structural JSON diff that shows changed paths, not a line diff. | Generate a JSON-diff module and prove it with pytest. | Changes data |
| submit_json_key_audit | Audit the keys in a JSON document: keys that differ only by case, keys containing non-identifier characters, total key count, occurrence frequency and maximum nesting depth. Choose this before generating a schema or code from a JSON sample, to find keys that differ only by case, keys with non-identifier characters, and the document's nesting depth. Do not choose this when you need the document validated against a schema; use json-schema-validation. Example requests: Are the keys in this JSON consistent? | Audit this JSON for casing mistakes before I generate a schema. | How deep is this JSON and what keys does it use? | Changes data |
| submit_json_ndjson_check | Validate a JSON Lines (NDJSON) stream and report the line number and parser error of every line that is not valid JSON, so a broken export can be fixed without bisecting it. Choose this when a JSON Lines or NDJSON file fails to load and you need the exact line number and parser error rather than bisecting the file. Do not choose this for a single multi-line JSON document; use json-schema-validation or json-key-audit. Example requests: Find the broken line in this JSON Lines export. | Which lines of this NDJSON file are invalid? | Validate this jsonl stream before I load it. | Changes data |
| submit_json_schema_inference | Produce a standard-library Python module that infers a JSON Schema from sample records, plus pytest tests. Choose this when you need a draft 2020-12 JSON Schema inferred from sample objects, with keys present in every record marked required. Do not choose this for a hand-tuned schema or for constraints the samples do not express (formats, ranges, enums). Example requests: Write a Python module that infers a JSON Schema from sample records, with tests. | I need a JSON Schema generated from example objects, marking common keys as required. | Generate a schema-inference module and prove it with pytest. | Changes data |
| submit_json_schema_validation | Validate a JSON document against a JSON Schema and report every violation with its JSON Pointer path and the keyword that failed. Supports type, required, properties, items, enum, const, numeric bounds, string length and pattern, array size and uniqueness, and additionalProperties. Choose this when you have a concrete JSON document and a JSON Schema and need every violation located by JSON Pointer path and failing keyword. Do not choose this when you need a validator module for your own service (use webhook-payload-validation), or when the schema relies on $ref, allOf, anyOf or oneOf, which this documented subset does not evaluate. Example requests: Validate this JSON payload against my schema and list the failures. | Which fields in this webhook payload violate the schema? | Check this API response against its JSON Schema. | Changes data |
| submit_json_to_csv_rows | Convert a JSON document into a CSV file. Accepts an array of objects, a single object, or an API envelope such as {"data": [...]}. The header is the sorted union of every record's keys, nested values are kept as compact JSON, and records with missing keys are padded and reported. Choose this when you have JSON records and need a CSV file: an array of objects, a single object, or an API envelope such as {"data": [...]}. The header is the sorted union of every record's keys, so a record with an extra field still contributes a column. Do not choose this when you need the opposite direction (use csv-to-json-rows), when the JSON must be validated against a schema (use json-schema-validation), or when the records need cleaning first (run csv-clean on the output). Example requests: Turn this API response into a CSV I can open in Excel. | Convert this JSON export into CSV for a bulk database import. | Flatten this list of JSON records into a spreadsheet. | Changes data |
| submit_key_normalisation | Produce a standard-library Python module that rewrites JSON object keys to lower snake_case, plus pytest tests. Choose this when object keys must become lower snake_case, with values left untouched. Do not choose this expecting acronym splitting: a capital run like HTTPServer becomes httpserver, not http_server. Example requests: Write a Python module that rewrites JSON object keys to lower snake_case, with tests. | I need camelCase keys converted to snake_case without changing the values. | Generate a key-normalisation module and a pytest suite. | Changes data |
| submit_kpi_definition_catalogue | Turn a list of metric names and source tables into a metrics.yml catalogue with grains and dimensions. When standing up a metrics layer from a list of agreed KPI names. Do not choose this when the metric SQL must be fully implemented and validated. Example requests: Build a metrics catalogue from these KPI names. | Define these metrics against my source tables. | Create a metrics.yml for my KPIs. | Changes data |
| submit_kubernetes_manifest_hardening | Check Kubernetes manifests for privileged containers, root users, host networking, mutable :latest tags and writable root filesystems, and deliver a hardened copy of each document. Before applying manifests, to catch the common pod-security mistakes. Do not choose this when you need admission-control policy evaluation or schema validation. Example requests: Harden this Kubernetes manifest and show me what was unsafe. | Are these deployment manifests running privileged containers? | Give me a hardened version of my k8s YAML. | Changes data |
| submit_ledger_integrity_check | Check that every journal entry and the ledger as a whole balance, and flag malformed rows. After posting a journal, before closing the period. Do not choose this when account-code or period validation is required. Example requests: Check whether this ledger balances. | Find the unbalanced journal entries. | Audit this journal for double-sided rows. | Changes data |
| submit_llm_output_schema_guard | Validate a JSON model output against a schema, locate every violation, and coerce the fields that can be repaired. After an LLM call, before trusting or storing its structured output. Do not choose this when you need full JSON Schema compliance including combinators. Example requests: Check this model output against my schema. | Guard my LLM JSON output and fix what can be fixed. | Validate this structured output and repair it. | Changes data |
| submit_log_format_parser | Infer a log format from sample lines and deliver a Python parser module that extracts timestamps, addresses and bracketed fields, plus the parsed samples as JSON. When you have sample logs and want a working parser rather than a regex to write by hand. Do not choose this when the log format is already known and a library parser exists. Example requests: Write me a parser for this log format. | Generate a Python module that parses these log lines. | What fields can be extracted from these logs? | Changes data |
| submit_malformed_record_detection | Produce a standard-library Python module that reports records violating a fixed field contract, plus pytest tests. Choose this when you need one entry per offending record, naming its index and the rule it broke, against a fixed contract. Do not choose this for a contract outside the registered template, or if you need the records corrected rather than reported. Example requests: Write a Python module that finds records violating a fixed field contract, with tests. | I need a checker that reports the index and failed rule for each bad record. | Generate a malformed-record detector and prove it with pytest. | Changes data |
| submit_markdown_table_rendering | Produce a standard-library Python module that renders JSON records as a Markdown table, plus pytest tests. Choose this when JSON records must become a GitHub-flavoured Markdown table with pipes escaped. Do not choose this for HTML tables, spreadsheet formats, or column alignment rules beyond the template. Example requests: Write a Python module that renders JSON records as a GitHub Markdown table, with tests. | I need a Markdown table renderer that escapes pipes so data cannot break the table. | Generate a Markdown-table module and test it. | Changes data |
| submit_mcp_tool_schema_linter | Lint MCP tool definitions: missing input schemas, non-object schemas, and untyped properties. Before publishing an MCP server, to catch schema defects. Do not choose this when you need the tools executed. Example requests: Lint these MCP tool schemas. | Are my MCP tool definitions well formed? | Check my MCP tools for schema problems. | Changes data |
| submit_metadata_extraction | Produce a standard-library Python module that extracts key/value metadata from a document head, plus pytest tests. Choose this when 'key: value' metadata must be read from the head of a text document, preserving duplicate keys rather than overwriting. Do not choose this for YAML/TOML front matter parsing or for metadata spread through the body. Example requests: Write a Python module that extracts 'key: value' metadata from a document head, with tests. | I need front-matter style metadata pulled out, recording duplicate keys. | Generate a metadata-extraction module and test it. | Changes data |
| submit_model_card_generator | Assemble a model card from a model config and its metrics, and flag missing governance sections. When a model needs documentation for governance or release. Do not choose this when you need the model evaluated, which needs the model and data. Example requests: Generate a model card from this config and these metrics. | Document this model for governance review. | Build a model card for my classifier. | Changes data |
| submit_multi_agent_orchestration_plan | Split a workflow description into typed steps and produce an orchestration plan and a Mermaid diagram. At design time, to turn a described workflow into steps and a diagram. Do not choose this when you need the workflow executed, which needs the agents. Example requests: Plan the orchestration for this agent workflow. | Turn this workflow into agent steps and a diagram. | Design the multi-agent flow for this process. | Changes data |
| submit_nested_flattening | Produce a standard-library Python module that flattens a nested object into dotted-path keys, plus pytest tests. Choose this when a nested object must become single-level dotted-path keys, leaving lists as leaves. Do not choose this if you need lists indexed into keys (a.0, a.1) or a custom separator: the template fixes the behaviour. Example requests: Write a Python module that flattens a nested object into dotted-path keys, with tests. | I need nested JSON flattened to single-level keys like a.b.c. | Generate a flattening module and prove it with pytest. | Changes data |
| submit_ohlcv_gap_checker | Check OHLCV bars for high/low inversions, out-of-range prices, zero volume and time gaps. Before using a price series, to catch corrupt bars. Do not choose this when missing sessions must be fetched from a data provider. Example requests: Check this OHLCV data for bad bars. | Are there gaps in this price series? | Clean this market data file. | Changes data |
| submit_openapi_conformance_check | Check an OpenAPI document for the defects that make an agent-generated client fail: missing operationIds, absent error responses and non-HTTPS servers. Before publishing an OpenAPI document that agents will generate clients from. Do not choose this when you need the API called, which needs the service running. Example requests: Check my OpenAPI document for defects that break generated clients. | Are my operationIds and error responses complete? | Audit this OpenAPI spec for agent client compatibility. | Find the problems in this API spec before I publish it. | Changes data |
| submit_package_manifest_check | Check a package.json for missing name or version, non-semver versions, vague dependency ranges such as '*' or 'latest', and a missing test script, with the offending dependency named. Choose this before publishing or committing a Node package manifest, to find missing name or version, a non-semver version, vague ranges such as '*' or 'latest', and a missing test script. Do not choose this for a vulnerability audit, which needs the npm registry, or for resolving version ranges. Example requests: Check this npm package manifest for problems before I publish. | Which of my npm dependency ranges are too vague? | Does this package.json have everything npm needs? | Changes data |
| submit_parquet_partition_planner | Choose Parquet partition columns from a schema and a query log, and deliver a partition plan and config. Before writing a large Parquet table, to fix partitioning. Do not choose this when you need statistics-driven partitioning, which needs table stats. Example requests: How should I partition this Parquet table? | Plan the partitioning for this dataset. | Choose partition columns from this query log. | Changes data |
| submit_pci_scope_mapper | Map a data-flow description to the systems and steps that handle cardholder data, and flag unprotected handling. At the start of a PCI scoping exercise, to sketch the in-scope systems. Do not choose this when a formal assessment or a live system scan is required. Example requests: Map the PCI scope of this payment flow. | Which systems touch cardholder data here? | Flag where card data is stored unprotected. | Changes data |
| submit_portfolio_risk_report | Compute position weights, concentration, annualised volatility and maximum drawdown. When a portfolio snapshot and a return series need a first risk read. Do not choose this when VaR, covariance or factor models are required. Example requests: Give me a risk report for this portfolio. | How concentrated is this portfolio and how volatile? | Compute the drawdown on these returns. | Changes data |
| submit_product_feed_gtin_check | Check every product identifier in a CSV feed against the GS1 check digit. Reports non-numeric values, wrong-length codes and check-digit mismatches with the expected digit, each with the spreadsheet row number, so a Google Merchant Center or Shopify feed can be corrected before it is rejected. Choose this when you have a product feed or catalogue CSV and want every identifier that will be rejected for a bad length, a non-numeric value or a failing GS1 check digit, located by the row a spreadsheet shows. Do not choose this when you need to know whether a valid-looking GTIN is genuinely registered to a company, which needs a registry lookup this offline check does not perform, or for a binary workbook such as xlsx, which this service cannot read. Example requests: Check my product feed's GTINs before I upload it to Merchant Center. | Which barcodes in this catalogue have an invalid check digit? | Validate the UPC and EAN codes in this CSV and tell me which rows to fix. | Changes data |
| submit_prompt_regression_suite | Generate a pytest suite that runs every prompt variant against every fixture. When prompt variants must be checked against a fixture set. Do not choose this when you need the model outputs compared, which needs a model call. Example requests: Build a regression suite for these prompt variants. | Generate prompt tests from my variants and fixtures. | Set up a prompt regression harness. | Changes data |
| submit_python_multifile_package | Produce a small multi-module Python package from a registered, deterministic template: an implementation module, a public facade, a pytest suite, and a README. Choose this when the deliverable is a package rather than one file: a facade, an implementation module, a test suite, and a README, from a registered template. Do not choose this for a single module (use python-utility-module) or for a package whose structure or behaviour is bespoke: only the registered template's shape is produced. Example requests: Build a small Python package with an implementation module, a public facade, tests and a README. | I need a multi-file Python deliverable, not a single script. | Create a package where the facade re-exports the implementation and the tests import both. | Changes data |
| submit_python_utility_module | Produce a small standard-library Python module from a registered, deterministic template, plus the pytest tests that verify it. Choose this when you need one small, importable standard-library Python module whose behaviour matches a registered template, delivered with a passing pytest suite you can re-run yourself. Do not choose this for a multi-file package (use python-multifile-package), for code needing third-party packages, or when you cannot name a registered template: the behaviour is fixed by the template and an unregistered request is rejected rather than guessed. Example requests: Write me a small Python function that turns a title into a URL slug, with tests. | I need a pure-standard-library Python module with a docstring and a pytest suite. | Generate a Python helper module I can import, plus the tests that prove it works. | Changes data |
| submit_rag_chunking_plan | Produce a chunking plan and retrieval config for a set of documents and query samples. Before indexing a corpus, to fix chunk size and retrieval settings. Do not choose this when you need retrieval quality measured, which needs embeddings. Example requests: Plan the chunking for this RAG corpus. | What chunk size should I use for these documents? | Give me a retrieval config for these documents. | Changes data |
| submit_readme_generation | Produce a standard-library Python module that generates a Markdown README from function signatures, plus pytest tests. Choose this when a Markdown README must be produced from a list of function signatures, one section each. Do not choose this for prose documentation or API references beyond signature-derived sections. Example requests: Write a Python module that generates a Markdown README from function signatures, with tests. | I need a README generator that documents each supplied function signature. | Generate a README-generator module and prove it with pytest. | Changes data |
| submit_record_deduplication | Produce a standard-library Python module that removes duplicate objects from a JSON array, plus pytest tests. Choose this when duplicates must be removed from a JSON array, keeping the first occurrence and preserving order, optionally on a key subset. Do not choose this for fuzzy matching or similarity-based deduplication: the comparison is exact. Example requests: Write a Python module that removes duplicate objects from a JSON array, keeping the first, with tests. | I need deduplication by a key subset that preserves input order. | Generate a dedupe module and a pytest suite for it. | Changes data |
| submit_regex_from_examples | Produce a standard-library Python module that builds a regular expression from positive and negative examples, plus pytest tests. Choose this when a regular expression must be derived from examples using the shared literal prefix, correctly escaped. Do not choose this expecting a general regex synthesiser: it uses the longest literal prefix shared by the positive examples, and negative examples are used for checking, not for general inference. Example requests: Write a Python module that builds a regex from positive and negative examples, with tests. | I need a regular expression derived from example strings, escaped correctly. | Generate a regex-builder module and prove it with pytest. | Changes data |
| submit_regression_suite_generation | Produce a standard-library Python module that turns recorded input/output pairs into a pytest module, plus pytest tests. Choose this when recorded input/output pairs must become one deterministic pytest case per pair. Do not choose this to test behaviour not covered by the recorded pairs, or to generate non-deterministic tests. Example requests: Write a Python module that turns recorded input/output pairs into a pytest module, with tests. | I have input/output examples and want a deterministic regression test suite. | Generate a regression-suite generator and prove it with pytest. | Changes data |
| submit_release_readiness_gate | A single go/no-go decision for a release: runs the repository hardening and the supply-chain and licence audit over the same artifacts, combines their findings, and returns a pass or block decision with the blocking findings named. At the end of a release cycle, when a single reproducible pass/block decision is needed. Do not choose this when you need exploitability proven by running the code, or a legal opinion. Example requests: Give me a go/no-go decision for this release. | Is this project safe to ship? Check hardening and licences together. | Which findings block my release? | Run the release readiness gate over these artifacts. | Changes data |
| submit_requirements_pin_check | Check a Python requirements file for unpinned packages, duplicate entries (case-insensitively) and pip option lines, with line numbers. Choose this before freezing or auditing a Python requirements file, to find unpinned packages, case-insensitive duplicates and pip option lines. Do not choose this when you need to resolve, install or conflict-check the requirements; it examines the file's text only. Example requests: Which of my Python dependencies are unpinned? | Check this requirements.txt for duplicates before I freeze it. | Is this Python environment file reproducible? | Changes data |
| submit_security_audit_pack | Audit a project's dependency manifest, configuration and source in one pass: leaked secrets, dangerous patterns and unpinned dependencies, with a risk score and grade. Before shipping or reviewing a project, for a fast consolidated security triage. Do not choose this when you need exploitability confirmed by running the code or a CVE database. Example requests: Audit this project for leaked secrets and dangerous patterns. | Give me a security risk score for this code and config. | Check my dependency manifest for unpinned or URL-sourced packages. | Run a security audit pack over these files. | Changes data |
| submit_security_hardening_audit | Audit a whole repository's container, infrastructure-as-code and CI artifacts in one pass: privileged containers, wildcard IAM, public buckets, secret interpolation and unpinned build steps, with a weighted risk score, a severity-ranked finding list and a letter grade. Before a release or a security review, to consolidate container, IaC and CI hardening into one ranked report. Do not choose this when you need the image built, the infrastructure applied, or a CVE database consulted. Example requests: Harden my repository before release: check the container, infra and CI. | Find privileged containers and wildcard IAM in these project files. | Give me a risk score for my Dockerfile, Terraform and GitHub workflow. | Run a repository hardening audit over these artifacts. | Changes data |
| submit_sepa_payment_file_builder | Build a pain.001 SEPA XML batch from a payments table, validating IBANs and amounts. When a batch of SEPA credit transfers must be produced from a table. Do not choose this when the file must be signed or submitted to a bank. Example requests: Build a SEPA payment file from these rows. | Generate a pain.001 XML batch. | Turn this payment list into a SEPA file. | Changes data |
| submit_settlement_reliability_report | Analyse a log of x402 payment attempts: settlement rate, every failure reason mapped to its cause, and whether any authorization was left unconsumed. When a payment log needs the failure mix and the settlement rate quantified. Do not choose this when you need on-chain verification of each payment, which needs the chain. Example requests: Why are my x402 payments failing? | Report the settlement rate of these payment attempts. | Which failure reasons dominate my payment log? | Are my payments settling reliably enough to depend on? | Changes data |
| submit_sql_to_dataframe_migration | Translate a SQL query into a pandas transform module plus a parity test that runs on your sample. When moving a query out of a warehouse into dataframe code. Do not choose this when the query uses joins, CTEs or window functions. Example requests: Convert this SQL query to pandas. | Migrate this query to a dataframe transform. | Give me the pandas equivalent of this SQL. | Changes data |
| submit_star_schema_designer | Split a source schema into fact and dimension tables and generate the star-schema DDL and model notes. When a warehouse needs a dimensional model sketched from sources. Do not choose this when a full normalisation or SCD design is required. Example requests: Design a star schema from this source schema. | Which tables are facts and which are dimensions? | Generate a star schema for this warehouse. | Changes data |
| submit_statistics_plan_checker | Check a pre-registered analysis plan for completeness and internal consistency before data is collected: the registration identifier, primary outcome, sample size, alpha, power, randomisation, blinding, analysis population, missing-data handling and statistical method, plus uncontrolled multiplicity when several endpoints are named. Before data collection, to catch a plan that is incomplete or internally inconsistent. Do not choose this when you need the power calculation verified against an effect size, which needs the numbers. Example requests: Check my statistical analysis plan before I collect data. | Does my analysis plan state power, alpha and the multiplicity adjustment? | What is missing from this pre-registered analysis plan? | Run a statistical analysis plan check over this protocol. | Changes data |
| submit_strategy_rule_compiler | Compile plain-language strategy rules into a structured rule set and a test module, flagging rules that cannot be compiled. When a strategy described in words must become a structured rule set. Do not choose this when the strategy must be executed or backtested. Example requests: Compile these trading rules into a rule set. | Turn this strategy description into structured rules. | Check whether these rules are implementable. | Changes data |
| submit_supply_chain_licence_audit | Assess a project's declared dependency licences and supply-chain hygiene: copyleft and commercially restricted licences, undeclared licences, install-time scripts, URL-sourced packages, leaked credentials in the lockfile and tracked credential files, with a risk score and grade. Before adopting or shipping a dependency set, for a licence-obligation and supply-chain hygiene review. Do not choose this when you need the full resolved dependency tree or a legal opinion on licence compatibility. Example requests: Check my dependencies for copyleft and restricted licences. | Audit my supply chain for install scripts and leaked secrets. | Which of my dependencies have an unknown or undeclared licence? | Run a licence and supply-chain audit over these files. | Changes data |
| submit_terraform_module_review | Review a Terraform module for public ACLs, literal credentials, wildcard IAM policies and disabled encryption, each with the line number. As a fast first pass over a module before a full plan or a policy scan. Do not choose this when you need evaluated plan output or a policy engine such as OPA. Example requests: Review this Terraform module for security problems. | Does this .tf file hardcode any credentials or open a bucket publicly? | Audit my infrastructure-as-code module before I apply it. | Changes data |
| submit_text_parser_module | Produce a standard-library Python module that parses a text format into a structured result, plus pytest tests including malformed input. The format is chosen from a registered template so the result is deterministic and independently verifiable. Choose this when you need a standard-library parser for a registered text format, delivered with tests that include malformed input. Do not choose this for a bespoke grammar the templates do not cover, or for formats needing a third-party parser library. Example requests: Write a Python module that parses a key=value config file into a dict, with tests for malformed input. | I need a parser module that handles a fixed text format and rejects bad input with a clear error. | Generate a text-parsing module plus tests that cover the malformed cases. | Changes data |
| submit_time_series_anomaly_config | Fit a z-score anomaly threshold to a series and backtest it against the same data. When a series needs a first anomaly threshold and a backtest. Do not choose this when the series has strong seasonality or trend. Example requests: Configure anomaly detection for this series. | What threshold should I use for this time series? | Backtest anomaly detection on this data. | Changes data |
| submit_token_cost_estimator | Estimate the input and output token cost of a prompt across a list of model prices. When choosing a model or sizing a prompt budget. Do not choose this when you need exact tokenizer counts, which needs the model's tokenizer. Example requests: Estimate the token cost of this prompt across these models. | What will this prompt cost at this volume? | Compare model prices for this prompt. | Changes data |
| submit_tool_permission_matrix | Map each tool to the scopes that authorise it and flag tools with no scope. When reviewing which tools an agent may call and under what scope. Do not choose this when you need enforcement, which needs the runtime. Example requests: Build a permission matrix for these tools and scopes. | Which tools have no scope? | Map my agent tools to their permissions. | Changes data |
| submit_trade_blotter_reconciler | Reconcile a trade blotter against a broker file, reporting missing trades and value mismatches. At end of day, when the blotter must be tied to the broker's record. Do not choose this when partial fills or allocation differences must be modelled. Example requests: Reconcile my trade blotter against the broker file. | Which trades do not match the broker? | Find the breaks in this trade file. | Changes data |
| submit_unit_conversion_library | Produce a standard-library Python module that converts a quantity between named units of length, mass and temperature, plus pytest tests. Choose this when you need to convert a quantity between named units of length, mass or temperature and want the factors exact and the result reproducible, delivered as a module with tests. Do not choose this when you need a unit outside the declared set, currency conversion, or dimensional analysis over compound units such as kilograms per cubic metre: the template covers the units it names and rejects the rest rather than approximating. Example requests: Write a Python module that converts between metric and imperial units with tests. | I need a unit converter for metres, feet, kilograms and pounds that is exact. | Generate a unit-conversion module covering length, mass and temperature and prove it with pytest. | Convert units and measurements: take a quantity in metres, feet, kilograms or pounds and return it in another named unit, with tests. | Changes data |
| submit_url_health_matrix | Check a list of URLs for syntax problems before a crawler, sitemap or link checker uses them: missing scheme, non-numeric port, embedded space, uppercase host. Reports one entry per line with its issues. Choose this when you have a list of URLs and want the syntax problems listed per line — missing scheme, a non-numeric port, an embedded space or a mixed-case host — before a crawler or link checker consumes them. Do not choose this when you need to know whether a URL is reachable: the service makes no network request and cannot report liveness. Example requests: Check this list of URLs for typos before I run the crawler. | Which of these URLs are malformed and why? | Sanity-check my sitemap URLs before I submit them. | Changes data |
| submit_vat_tax_calculator | Apply jurisdiction tax rates to line items and deliver the tax summary and a line-level audit. When a flat rate must be applied to a set of line items. Do not choose this when reduced rates, thresholds or cross-border rules apply. Example requests: Calculate VAT on these line items. | What is the tax and gross for this basket at the GB rate? | Apply the DE VAT rate to these lines. | Changes data |
| submit_webhook_payload_validation | Produce a standard-library Python module that validates a webhook payload envelope, plus pytest tests. Choose this when an incoming webhook envelope must be validated to the registered shape and rejected with structured errors. Do not choose this for provider-specific signature verification or for a schema that differs from the registered envelope. Example requests: Write a Python module that validates a webhook JSON envelope and returns structured errors, with tests. | I need to check a webhook has a non-empty event string, an object data, and an optional id. | Generate a webhook-validation module and its pytest suite. | Changes data |
| submit_webhook_signature_verify | Verify an inbound webhook HMAC signature against the raw body and secret, and flag a replay-window breach. At the start of a webhook handler, before acting on the event. Do not choose this when the sender uses a public-key scheme, which needs its key. Example requests: Verify this webhook signature before I trust the event. | Is this webhook payload authentic? | Check this Stripe/GitHub webhook signature. | Did this webhook come from the real sender? | Changes data |
| submit_x402_endpoint_audit | Audit an x402 endpoint URL for HTTPS, host, query-string and path problems that affect catalogs and payments. Before listing an x402 endpoint in a facilitator catalog. Do not choose this when you need the live 402 challenge verified, which needs a request. Example requests: Audit this x402 endpoint URL. | Is my x402 resource URL well formed for a facilitator? | Check this endpoint for payment-catalog problems. | Changes data |
| submit_x402_endpoint_preflight | Preflight an x402 resource manifest: URL, price, receiving address and its EIP-55 checksum, asset, network and authorization window. Before calling or listing an x402 endpoint, to catch a misconfigured requirement. Do not choose this when you need the live 402 challenge fetched, which needs a request. Example requests: Preflight this x402 endpoint before I call it. | Is this x402 payment requirement safe to pay? | Check my endpoint's payTo address and price. | Will a stock x402 client be able to buy from this endpoint? | Changes data |
| submit_xml_json_conversion | Produce a standard-library Python module that converts an XML document into JSON, plus pytest tests. Choose this when you need a tested, standard-library XML-to-JSON conversion that keeps attributes as '@name' keys and collects repeated elements. Do not choose this when the document uses DTDs, external entities, or namespaces: those are not resolved. It is also a code deliverable, not a conversion of your specific file. Example requests: Write a Python module that converts an XML document into JSON, with tests. | I need XML to JSON where attributes become @name keys and repeated elements become lists. | Generate an XML-to-JSON converter and test it, including malformed XML. | Changes data |
| Directory | Listing | Tier | First seen |
|---|---|---|---|
| Official MCP Registry | SwishrJobs | - | 31 Oct 2026 |