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docs-codebase文档代码库

Agent Skill

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

总安装

2,928

周安装

122

GitHub Stars

60

下载量

976
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:docs-codebase(文档代码库)
来源仓库:https://github.com/vasilyu1983/ai-agents-public
仓库路径:skills/docs-codebase
安装命令:
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill docs-codebase
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill docs-codebase

简介

提供面向代码库的技术文档编写模式和现代最佳实践指导。

  • 涵盖 docs-as-code、审查节奏、AI 辅助起草等 2026 年标准方法。
  • 包含 README、ADR、API 参考等多种文档类型的模板。
  • 适用于希望建立可持续文档体系的开发团队。docs-codebase 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 强调生成引擎优化(GEO)以提升 AI 搜索可见性。

SKILL.md

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 TypeTemplateWhen to Use
Project READMEreadme-template.mdNew project, onboarding
Architecture Decisionadr-template.mdTechnical decisions
API Referenceapi-docs-template.mdREST/GraphQL APIs
Changelogchangelog-template.mdVersion history
Contributing Guidecontributing-template.mdOpen 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:
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
  1. For each topic/feature, keep exactly one canonical spec/reference and merge all duplicates into it.
  2. Keep only durable facts/decisions in canonical docs; move exploration detail to short linked evidence notes.
  3. Keep a compact LLM doc library with root anchors: AGENTS.md, README.md, and minimal canonical docs under docs/ (instructions, specs, reference-data).
  4. Delete integrated drafts on schedule; avoid .archive/ mirrors in docs/ unless retention is mandatory.

Decision Tree

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.

# 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:

---
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:

GapStatusSealed In
Chart aspects visible to freeSealedPR #26
Dreams unlimited for freeSealedPR #26
Ask Cosmos no rate limitOpen

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):

> 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):

# 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 humans and agents.

Sync Rules

  1. Update one canonical status source first (feature matrix / roadmap / decision log).
  2. Propagate only by reference links in secondary docs; avoid duplicate status prose.
  3. Add concrete completion dates and owner for status changes.
  4. If temporary reports are integrated, mark lifecycle state (integrated or superseded) and delete_by.

Release Gate

A feature is not doc-complete until:

  • canonical status doc updated,
  • dependent docs checked for conflicting claims,
  • docs sync checklist completed.

Resources

ResourcePurpose
references/readme-best-practices.mdREADME structure, badges
references/adr-writing-guide.mdADR lifecycle, examples
references/changelog-best-practices.mdKeep a Changelog format
references/api-documentation-standards.mdREST, GraphQL, gRPC docs
references/code-commenting-guide.mdDocstrings, inline comments
references/contributing-guide-standards.mdCONTRIBUTING.md structure
references/docs-as-code-setup.mdMkDocs, Docusaurus, CI/CD
references/writing-best-practices.mdClear communication
references/markdown-style-guide.mdMarkdown formatting
references/documentation-testing.mdVale, markdownlint, cspell
references/ai-documentation-tools.mdMintlify, DocuWriter, GEO
references/production-gotchas-guide.mdDocumenting platform issues
references/documentation-metrics.mdDoc quality, freshness, coverage scoring
references/onboarding-documentation.mdDeveloper ramp-up guides, Day 1-Week 4
references/runbook-writing-guide.mdOperational runbooks, incident response
references/backlog-status-sync-pattern.mdCanonical backlog status sync workflow for multi-doc repos

Templates

Related Skills

SkillPurpose
qa-docs-coverageDocumentation gap audit
dev-api-designREST API patterns
dev-git-workflowConventional Commits
docs-ai-prdPRD templates

Fact-Checking

  • Use web search/web fetch to verify current external facts, versions, pricing, deadlines, regulations, or platform behavior before final answers.
  • Prefer primary sources; report source links and dates for volatile information.
  • If web access is unavailable, state the limitation and mark guidance as unverified.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

29.76%
按下载量换算290

Cursor

20.01%
按下载量换算195

Gemini CLI

16.08%
按下载量换算157

Antigravity

11.58%
按下载量换算113

OpenCode

7.81%
按下载量换算76

Codex

3.53%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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