Bruno API

Bruno API Documentation Generator Skill

Inputs & Modes

This Skill expects one of:

• A path to a single Bruno file (usually *.bru), OR
• --scan <dir> to analyze all .bru files under a directory.

Optional flags:

• --dry-run – produce an analysis plan only (no deep codebase search).
• --output <path> – write the generated markdown documentation to a file.

If inputs are missing or ambiguous, ask the user to confirm:

• Which .bru file(s) to analyze.
• Whether they want --dry-run or full documentation.
• Whether an output file should be written.

Output Shape & Severity Tags

Dry-run output

Return a short plan containing:

• Endpoint summary: method, URL, auth, and any detected params/body.
• Where you will look in the Django codebase (specific file paths/directories).
• Which documentation sections will be generated.
• Complexity notes (e.g., “DRF ViewSet + serializer” vs “Ninja router + schema”).

Full documentation output

Generate a single markdown document for each endpoint using this structure:

• # <Endpoint Name>
• <METHOD> <URL Pattern>
• Authentication, Permissions, Multi-tenant
• ## Overview
• ## Request (headers + params/body with types/validation)
• ## Response (success example + common error cases)
• ## Implementation Details (URL config + view + serializer/schema; always with file.py:line)
• ## Business Logic (step-by-step, include side effects like tasks/external calls)
• ## Frontend Integration (TypeScript types + call example + React Query hook example)
• ## Testing (Bruno tests + edge cases + required fixtures/data)
• ## Notes (perf considerations, related endpoints, rollout notes)

Use severity tags only when something prevents correctness/completeness:

• [BLOCKING] – cannot locate the endpoint implementation or critical auth/permission logic.
• [SHOULD_FIX] – documentation gaps due to missing/incomplete source details (e.g., response shape unclear).
• [NOTE] – optional improvements, related endpoints, refactors, or performance observations.

Workflow

Step 1 — Parse the Bruno file(s)

For each .bru file:

• Extract:
  • HTTP method
  • URL / path pattern
  • Headers
  • Query parameters
  • Path parameters (from the URL pattern)
  • Request body (and infer a schema where possible)
• Detect authentication intent:
  • JWT / token headers
  • Session/cookie usage
  • Explicit “no auth” signals
• Capture any Bruno test/assert blocks as testing hints.

Step 2 — Locate the Django route & implementation

Treat these repo conventions as first-class when present:

• If the URL starts with /api/v2/:
  • Check dashboardapp/v2_urls.py.
  • Check dashboardapp/views/v2/ for the view/viewset.
• If the URL starts with /api/v2/pulse/:
  • Check pulse_iq/api/ for Django Ninja routers/endpoints.
• Otherwise:
  • Search app-level urls.py modules for the path prefix.
  • If needed, Grep for a distinctive path segment from the Bruno URL.

Once the route is found, identify the implementation type:

• DRF
  • View / ViewSet class and handler method (list, retrieve, create, custom actions).
  • Serializer(s) used (including nested serializers) and validation rules.
  • Permissions / authentication classes.
  • Queryset and filtering (especially company/org scoping).
• Ninja
  • Router and endpoint function.
  • Pydantic schema(s) and validation.
  • Auth configuration/decorators.
  • Multi-tenant scoping and access control.

Always record code references with line numbers (path/to/file.py:123).

Step 3 — Extract behavior and contracts

For the located endpoint:

• Summarize the business purpose and any key invariants.
• Document validation and error behavior:
  • Common 400 reasons (schema/serializer validation).
  • Auth failures (401) and permission failures (403).
  • Not-found cases (404) and domain-specific error cases.
• Identify multi-tenant constraints:
  • How company/org is inferred (JWT claims, request context, URL param).
  • Which queryset filters enforce scoping.
• Note side effects:
  • Background tasks (Celery), emails, webhooks, external service calls.
  • Writes to critical models and any transactional boundaries.

Step 4 — Generate documentation

Write the markdown doc per “Full documentation output”.

Rules:

• Prefer precise types over “string/number” when you can infer them.
• Include at least one realistic example request and success response.
• If response shape is dynamic or large, document the stable contract and include a representative sample, not the entire universe of fields.
• When you’re unsure, be explicit about assumptions and mark with [SHOULD_FIX].

Step 5 — Handle --output and --scan

• If --scan <dir>:
  • Find all .bru files recursively under that directory.
  • Generate one markdown doc per file.
  • If no --output is provided, return docs in the response (grouped by file).
• If --output <path> is provided:
  • Write output to that path.
  • If scanning multiple files, either:
    • Write a single combined doc (with a clear table of contents), OR
    • Write multiple files under an output directory (ask the user which they want).

Compatibility Notes

This Skill is designed to work with both Claude Code and OpenAI Codex.

• Claude Code: install the corresponding plugin and use its slash commands (see plugins/bruno-api/commands/).
• Codex: install the Skill directory and invoke name: bruno-api.

For installation, see this repo's README.md.