docs-codebase
Technical writing for READMEs, ADRs, API docs, and changelogs. Use when revising or consolidating a repo documentation folder.
What this skill does
# Technical Documentation
Execution-ready patterns for clear, maintainable technical documentation.
**Modern best practices (January 2026)**: docs-as-code, ownership + review cadence, documentation QA gates (links/style/spelling), AI-assisted drafting + review, OpenAPI 3.2.0 where streaming schemas matter, and GEO (Generative Engine Optimization) for AI search.
## Quick Reference
| Documentation Type | Template | When to Use |
|-------------------|----------|-------------|
| **Project README** | [readme-template.md](assets/project-management/readme-template.md) | New project, onboarding |
| **Architecture Decision** | [adr-template.md](assets/architecture/adr-template.md) | Technical decisions |
| **API Reference** | [api-docs-template.md](assets/api-reference/api-docs-template.md) | REST/GraphQL APIs |
| **Changelog** | [changelog-template.md](assets/project-management/changelog-template.md) | Version history |
| **Contributing Guide** | [contributing-template.md](assets/project-management/contributing-template.md) | Open source, teams |
## Workflow
1. Identify the documentation type and audience.
2. Find existing patterns in the repo; follow local conventions.
3. Start from the closest template in `assets/` and adapt.
4. Add ownership + review cadence for critical docs (runbooks, onboarding, API reference).
5. Run documentation QA (links, formatting, spelling, examples) before merging.
### Docs Folder / LLM-Generated Revamp Mode (Any Repo)
Use this mode when a repo's `docs/` folder contains substantial research notes for LLMs and implementation docs generated by LLMs.
1. Build an inventory and classify each file by doc type (`Tutorial`, `How-to`, `Reference`, `Explanation`).
2. Mark lifecycle metadata for non-canonical files:
```yaml
status: draft | canonical | integrated | superseded
owner: @username
last_verified: 2026-02-24
integrates_into: docs/path/to/canonical-doc.md
delete_by: 2026-03-31
```
3. For each topic/feature, keep exactly one canonical spec/reference and merge all duplicates into it.
4. Keep only durable facts/decisions in canonical docs; move exploration detail to short linked evidence notes.
5. Keep a compact LLM doc library with root anchors: `AGENTS.md`, `README.md`, and minimal canonical docs under `docs/` (`instructions`, `specs`, `reference-data`).
6. Delete integrated drafts on schedule; avoid `.archive/` mirrors in `docs/` unless retention is mandatory.
## Decision Tree
```text
User needs: [Documentation Task]
├─ Repo has a docs folder with many LLM-generated docs? → **Revamp Mode** (inventory → canonicalize → trim)
├─ New project? → **README.md**
├─ Technical decision? → **ADR**
├─ Building API? → **OpenAPI spec** + api-docs-template
├─ New version? → **CHANGELOG.md**
├─ Team collaboration? → **CONTRIBUTING.md**
├─ Documenting code? → **Docstrings** (JSDoc, Python)
└─ Building docs site? → **MkDocs** (Python) or **Docusaurus** (JS)
```
## Cross-Platform AI Documentation
### AGENTS.md Standard
Prefer `AGENTS.md` as the cross-tool source of truth. If a specific tool requires a different filename (example: Claude Code uses `CLAUDE.md`), keep it aligned via a symlink only when you want identical content across tools.
```bash
# If `CLAUDE.md` does not exist and you want identical content:
ln -s AGENTS.md CLAUDE.md
```
## Do / Avoid
### Do
- Assign owners and review cadences to critical docs
- Add CI checks for links, style, and staleness
- Prefer small, task-oriented docs over big wiki pages
- Use Keep a Changelog format with semantic versioning
### Avoid
- Docs without owners (guaranteed to rot)
- Stale runbooks (dangerous during incidents)
- Copy/paste docs that drift from code
## LLM-First Documentation Patterns
When documentation is consumed primarily by AI agents (AGENTS.md, CLAUDE.md, canonical docs for coding assistants), stale docs become a distinct category of bug.
### Stale Docs = Agent Bugs
An agent reading stale docs will:
- Attempt to fix problems that are already solved (e.g., "9 open gating gaps" that were all sealed)
- Use wrong model names (e.g., "Claude Haiku" when code uses `gpt-4o`)
- Apply wrong limits (e.g., "fully gated" when free tier actually gets 3/week)
- Re-implement features that already exist
**Rule:** Treat doc updates as part of the feature PR, not as a follow-up task.
### Report Integration Lifecycle
Temporary investigation docs (QA reports, research exports, audit findings) must not become permanent false sources of truth.
Every dated report file must carry lifecycle metadata:
```yaml
---
Status: pending-integration | integrated | superseded
Integrates-into: docs/product/pricing-feature-matrix.md
Owner: @username
Delete-by: 2026-03-15
---
```
Workflow:
1. Create report with `Status: pending-integration`
2. Extract durable findings into canonical docs
3. Mark report `Status: integrated` with date
4. Delete after `Delete-by` date (git history preserves everything)
### Living Docs: Audit Tables with Status Columns
Instead of deleting audit findings, add a Status column:
| Gap | Status | Sealed In |
|-----|--------|-----------|
| Chart aspects visible to free | Sealed | PR #26 |
| Dreams unlimited for free | Sealed | PR #26 |
| Ask Cosmos no rate limit | Open | — |
This preserves the audit trail while showing current state. Agents can quickly scan for `Open` items.
### Two-Pass Consolidation
When consolidating planning docs into canonical docs:
1. **First pass:** Follow the plan — extract content, delete source files, fix cross-references
2. **Second pass:** Audit deleted content against canonical destinations
- `git show` deleted files to recover any unique data missed in planning
- Compare code to docs for drift (e.g., feature marked "Planned" but code shows it's implemented)
Even thorough consolidation plans miss unique data that only lived in one source doc.
### Canonical Set Rule (No Doc Sprawl)
- One subject/feature should have one canonical doc.
- Derived docs must link to canonical docs instead of restating them.
- If a derived doc is fully integrated, mark it `integrated` and remove it by `delete_by` (default: delete, not archive).
- If two canonical docs overlap, merge and leave a redirect note in the removed file path.
### Canonical LLM Library Rule
- Root files are mandatory anchors: `AGENTS.md` for agent behavior/instructions and `README.md` for project navigation.
- The `docs/` folder should expose only a small canonical set for LLM consumption: current instruction sets, current specs, and durable reference data.
- Research logs, exploratory prompts, and intermediate drafts are temporary working files, not library entries.
- Keep discovery breadcrumbs as links from canonical docs; do not duplicate full research dumps.
### Anti-Fluff Rewrite Gate
Before merging LLM-generated docs, require:
- explicit audience and decision/use-case for each section
- measurable statements instead of vague claims
- source links + dates for external facts
- removal of duplicated paragraphs and "future ideas" not tied to a tracked decision
### Staleness Disclaimers Over Wrong Numbers
For externally-sourced data (competitor pricing, API rate limits, third-party capabilities):
```markdown
> Prices as of Feb 2026 — verify current pricing at [source].
```
A staleness disclaimer is safer than a potentially wrong number. Wrong numbers in agent-consumed docs cause incorrect implementation decisions.
### Decision Log Collision Prevention
When adding entries to a decision log (e.g., `### D039 — Feature Name`):
```bash
# Always check the latest entry number before adding
grep -o '### D[0-9]*' docs/decision-log.md | tail -1
```
Numbering collisions happen when two decisions are logged in rapid succession without checking.
## Backlog Status Sync Pattern (Mandatory)
When implementation status changes (for example backlog milestones completed), sync canonical docs in the same delivery cycle to prevent stale guidance for humaRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.