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Documentation

skill-tahirraufkeeyu-software-development-agent-stack-sdas-documentation · by tahirraufkeeyu

Use when the user asks for a README, API docs, an ADR, a changelog, a runbook, or any structured technical write-up. Generates the requested artifact from the codebase and git history, following widely-adopted conventions (Keep a Changelog, MADR ADRs, OpenAPI-driven API docs).

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Install

$ agentstack add skill-tahirraufkeeyu-software-development-agent-stack-sdas-documentation

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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4mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

When to use

  • "Write a README for this repo."
  • "Generate API docs from the OpenAPI spec" or "from these route files".
  • "Write an ADR for choosing Kafka over SQS."
  • "Produce a CHANGELOG entry for v1.4.0."
  • "Document this module / this CLI command."

Do not use this skill for marketing copy, blog posts, or long-form narrative — those are outside its scope.

Inputs

The artifact type — one of:

  • readme
  • api-docs
  • adr
  • changelog
  • module-doc
  • runbook

Plus inputs specific to the type (see Procedure).

Outputs

Markdown files placed at the repo's conventional location:

  • README.md at repo root.
  • docs/adr/NNNN-title.md for ADRs (sequential zero-padded number).
  • CHANGELOG.md at repo root (Keep a Changelog format).
  • docs/api/ for API reference.
  • docs/runbooks/.md for runbooks.
  • docs/modules/.md for module-level docs.

Tool dependencies

  • Read, Grep, Glob for codebase inspection.
  • Bash for git log, git tag, git diff.
  • Optional: openapi-cli / redocly to render OpenAPI.

Procedure

README

  1. Detect the project shape: languages (by file extensions), build tool (package.json, pyproject.toml, go.mod, Cargo.toml, Makefile), entry points, Dockerfile, CI config.
  2. Extract the one-sentence purpose from the existing top of README.md if present, from description in the manifest, or from a repo description. If none, ask the user.
  3. Draft sections in this order:
  • Title + one-sentence description.
  • Status badges (CI, coverage, license, latest release) — only ones that actually exist.
  • Quickstart: the three commands that take a new developer from clone to "it runs" (install, build/test, run). Verify commands by inspecting scripts; do not invent.
  • Usage: the two or three most common invocations with real examples.
  • Configuration: table of env vars with purpose, default, required-ness (read from .env.example, config schema, or source).
  • Development: test command, lint command, format command, pre-commit setup.
  • Deployment: link to runbook or pipeline if present.
  • Contributing: link to CONTRIBUTING.md if present; otherwise a short block (branch naming, commit style, PR review expectations).
  • License: one line, name and link.
  1. Keep the README under ~200 lines. Push depth to docs/.

API docs

  1. If an OpenAPI / GraphQL SDL file exists, use it as the source of truth. Render a Markdown reference grouped by resource; include request/response examples drawn from the spec's examples.
  2. Otherwise, walk route definitions (express.Router, FastAPI @app.get, go-chi mux.Get, axum Router::new().route) and extract method, path, summary (from docstring/handler comment), auth scope, and reference schemas.
  3. For each endpoint: path, method, description, auth, path params, query params, request body schema, response schemas per status code, example request, example response.
  4. Include a top-level section on auth (how to obtain a token), error envelope, pagination, versioning, rate limits — link to api-standards.md for the underlying conventions.

ADR (MADR format)

File: docs/adr/NNNN-short-title.md.

# NNNN. 

- Status: proposed | accepted | deprecated | superseded by ADR-XXXX
- Date: YYYY-MM-DD
- Deciders: 

## Context

## Decision

## Alternatives considered

- Option A: 
- Option B: 
- Option C (chosen): 

## Consequences

### Positive

- ...

### Negative

- ...

### Neutral / follow-ups

- ...

## References

- Links to related tickets, prior ADRs, external research.

Infer the next sequential number by reading docs/adr/. Start at 0001.

Changelog (Keep a Changelog)

Maintain CHANGELOG.md:

# Changelog

All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

### Added
### Changed
### Deprecated
### Removed
### Fixed
### Security

## [1.4.0] - 2026-04-19
...

Procedure:

  1. Read git log ..HEAD --pretty=format:'%h %s'.
  2. Group commits by Conventional Commits type:
  • feat -> Added or Changed.
  • fix -> Fixed.
  • perf -> Changed.
  • refactor / chore / docs / test -> omit unless user-visible.
  • BREAKING CHANGE: -> Removed or Changed, called out.
  1. Rewrite each entry in user-facing terms, not commit terms: "Added bulk export for invoices" not "feat(invoices): bulk export handler".
  2. Move [Unreleased] into the new version heading with the date; create a new empty [Unreleased].
  3. Update the bottom [version]: links.

Module doc

For a single module/package: purpose, public API, example usage, collaborators and dependencies, failure modes, instrumentation (metrics, logs), ownership.

Runbook

Every on-call runbook has: service name and owners, dashboard links, alert catalogue (each alert -> diagnosis steps -> mitigation), common incidents with step-by-step responses, escalation path, known risks. Avoid including credentials or exact thresholds that will drift — link to the source of truth.

Examples

Happy path: README for a Node CLI

Generated section (excerpt):

# inv-cli

Command-line client for the Invoices API.

## Quickstart

```bash
pnpm install
pnpm build
pnpm exec inv-cli invoices list --limit 5

Configuration

| Variable | Purpose | Default | Required | | --- | --- | --- | --- | | INV_API_URL | Base URL of the Invoices API | https://api.acme.example | no | | INV_API_TOKEN | Bearer token | — | yes | | INV_TIMEOUT_MS | Per-request timeout | 10000 | no |

Development

  • Test: pnpm test
  • Lint: pnpm lint
  • Format: pnpm format

License

MIT. See [LICENSE](LICENSE).


### Edge case: ADR for picking Kafka vs SQS

File `docs/adr/0007-choose-kafka-over-sqs.md`:

```markdown
# 0007. Choose Kafka over SQS for the invoice-events bus

- Status: accepted
- Date: 2026-04-19
- Deciders: platform team (Alex, Priya, Marco)

## Context

Invoice events drive billing, notification, and downstream analytics. The
current load is 2k events/s with peaks at 12k/s during end-of-month runs.
We need: (1) replay for the last 7 days, (2) per-customer ordering,
(3) at-least-once delivery, (4) consumer groups to fan out to 4 services.

## Decision

Use Kafka (MSK) as the event backbone. Partition by `customer_id`. Retention
7 days. Consumer groups per service.

## Alternatives considered

- Option A: SQS with FIFO queues. Pro: low ops. Con: per-group throughput cap
  (300 msg/s) insufficient for our largest customer; no replay.
- Option B: Kinesis. Pro: AWS-native, replay. Con: higher per-shard cost at
  our rates; weaker ecosystem of tooling (schema registry, ksqlDB).
- Option C (chosen): Kafka on MSK. Pro: partitioning, replay, ecosystem.
  Con: operational overhead of consumer-group lag monitoring and rebalance
  tuning.

## Consequences

### Positive
- Replay for debugging and backfills.
- Headroom to 100k msg/s without redesign.

### Negative
- On-call burden: consumer lag alerts, rebalance storms.
- Schema registry adds a deployment dependency.

### Neutral / follow-ups
- Write a runbook for consumer-lag triage (#4821).
- Evaluate tiered storage after 3 months.

## References

- Load test report: docs/loadtest/2026-03.md
- Ticket: PLAT-742

Constraints

  • Never invent commands. Verify every pnpm/make/go/python invocation against the repo.
  • Never fabricate env vars, endpoints, or config keys. Trace them back to source.
  • Never include secrets, tokens, internal URLs, or customer names in public docs.
  • Do not write marketing language ("blazing fast", "best-in-class"). State what the software does.
  • Do not claim test coverage numbers, SLA targets, or compliance certifications unless the user provides them.
  • Do not generate an ADR for a reversible trivial decision. ADRs are for decisions that are costly to reverse.

Quality checks

  • Every command in the README runs from a clean clone (mentally walk through it).
  • Every env var in the config table is referenced in the code.
  • ADR sections (Context, Decision, Alternatives, Consequences) are all present and each has real content.
  • CHANGELOG entries are user-facing, not commit-message-shaped.
  • Links are relative for in-repo references; absolute for external.
  • No emojis, no decorative headings, no sentences that could be deleted without losing information.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.