Claude Skills for Documentation
Claude skills for docs address one persistent failure: documentation gets written once at launch and then left alone until it has fully diverged from the code. A skill records how the document is structured and where its facts come from, so it can be regenerated on every meaningful change instead of rewritten by hand twice a year. The most durable case is the README and the API reference. Their content is derivable from the code: endpoints, parameters, call examples. Once that derivation is written down as a procedure, updating docs stops being a separate task someone has to remember and becomes a side effect of changing the code. This holds only while the skill names a source for every fact: documentation assembled from a model's memory goes stale just as quietly as documentation written by hand.
The second case is the changelog. A skill assembles it from commit history without retelling it verbatim: commits are written for developers, changelogs are read by users. The rule for translating one into the other is exactly what the skill contains.
The collection below covers READMEs, references, changelogs and project knowledge bases.
Skills in this collection
358 skills in this collection; the 24 highest-rated are listed below.
- Очистка и полировка пользовательских текстов — clean-user-facing-text is a Claude Code skill that audits and rewrites authorized natural-language text, removing suspicious invisible Unicode characters while preserving every fact, number, name, citation, and requirement. It is designed for use in Cursor when polishing articles, manuscripts, reports, documentation, emails, product copy, UI text, Markdown, or HTML prose. The workflow has two stages: a deterministic Unicode pass using bundled Python scripts (`inspect_text.py` and `clean_text.py`), and a prose rewrite that varies clause order, sentence rhythm, and connectors to replace formulaic phrasing with direct, natural wording. Code blocks, commands, paths, identifiers, formulas, verbatim quotations, and required disclosures are never modified. The skill does not support code-only tasks and explicitly does not claim detector evasion or proof of human authorship.
- Шаблон пользовательской slash-команды для Claude Code — example-command is a Claude Code skill that serves as a reference template for building custom slash commands using the `skills/<name>/SKILL.md` layout. It documents every supported frontmatter field — `name`, `description`, `argument-hint`, `allowed-tools`, and `model` — and shows how to handle required and optional arguments via the `$ARGUMENTS` variable. The skill is pre-approved to use the Read, Glob, Grep, and Bash tools. Functionally it is identical to the legacy `commands/example-command.md` format; only the file structure differs. Developers creating their own Claude Code plugins or slash commands can use this as a ready-made starting point instead of piecing together the syntax from documentation.
- Аналитика сессий Claude Code в HTML-отчёте — session-report is a Claude Code skill that analyzes session transcripts from ~/.claude/projects and produces a self-contained interactive HTML report covering token usage, cache efficiency, subagents, skills, and expensive prompts. The bundled analyze-sessions.mjs script collects data for a configurable window — 24h, 7d, 30d, or all time — and outputs a JSON with sections for overall, by_project, by_subagent_type, by_skill, cache_breaks, and top_prompts. The skill copies a bundled template (with pre-built JS/CSS for sorting, bars, and drill-downs), injects the JSON blob, then fills an anomalies block with 3–5 findings expressed as percentages of total tokens and an optimizations block with 1–4 actionable suggestions tied to specific projects or prompts. It's built for developers and teams who need visibility into Claude Code spending, want to spot cache-hit rates below 85%, or identify subagent calls averaging over 1M tokens per invocation.
- Аудит и улучшение CLAUDE.md файлов — claude-md-improver is a Claude Code skill that audits and improves CLAUDE.md files across an entire repository so Claude Code receives accurate, up-to-date project context. It operates in five phases: discovery of all CLAUDE.md, .claude.local.md, and nested variants via Glob and Bash; quality assessment of each file against six weighted criteria — commands/workflows, architecture clarity, non-obvious patterns, conciseness, currency, and actionability — scored A through F; generation of a detailed per-file report with a breakdown table; targeted diff-format improvement proposals with explanations; and Edit-based updates applied only after explicit user approval. The skill is designed for developers who want to fix stale build commands, document monorepo package relationships, or capture environment setup that Claude keeps missing — essentially turning CLAUDE.md maintenance into a structured, reviewable process.
- Шаблон для создания скилла Claude Code — example-skill is a Claude Code skill that serves as a reference template for building new plugin skills, covering their structure, file layout, and frontmatter format. The core SKILL.md file documents required and optional directory contents — including README.md, references/, examples/, and scripts/ subdirectories — along with frontmatter fields (name, description, version, license) and guidelines for writing effective trigger descriptions. It activates when a user asks to demonstrate skill format, create a skill template, or discusses plugin development patterns. Developers looking to build their own Claude Code skills will find clear explanations of how skills differ from commands and agents, plus concrete examples of trigger phrases that reliably invoke a skill.
- Глубокая документация кодовой базы для AI-агентов — deepinit is a Claude Code skill that performs deep codebase initialization by generating hierarchical AGENTS.md documentation across every project directory. It traverses the directory tree level by level — excluding node_modules, .git, dist, and similar folders — then analyzes each directory and writes a structured AGENTS.md covering key files, internal and external dependencies, code patterns, and special instructions for AI agents. Every file except the root includes a `<!-- Parent: ../AGENTS.md -->` tag, building a fully navigable hierarchy. On subsequent runs, auto-generated sections are updated while manual annotations below the `<!-- MANUAL -->` marker are preserved. The skill delegates work across specialized subagents (explore, architect, writer) and supports parallel processing of same-level directories. It suits teams that want their codebase to be immediately readable by AI tooling and easier to navigate for both agents and new developers.
- Составление стандартных операционных процедур — Преобразует текущую практику в стандартизированный SOP или политику с описанием ролей, пошаговых действий, контрольных точек и обработкой исключений. Результат — Word-документ, готовый к внедрению и аудиту.
- Деловые отчёты в Word по структуре — Business Reports is a Claude Code skill that drafts Word-format business reports following a fixed section order: executive summary, background, findings/analysis, recommendations, risks, and appendix. Reach for it when you need to turn raw notes or spreadsheet data into a polished document ready for leadership, when a memo or assessment needs consistent voice and properly sourced numbers, or when you're tired of manually styling headings and numbering every recommendation. Before writing a single sentence, the skill reads every attached or referenced file in full — figures, dates, and names come exclusively from those sources; if a source is silent on a point, the report says so rather than inventing data. Sections are rendered with real Word heading styles, real numbered lists, and properly formatted tables (header row, right-aligned numbers, units in the header), never simulated with bold text or tabs. Each recommendation links back to a specific finding and includes an owner and time frame when the source material allows it. The skill generates or rewrites documents rather than reviewing finished text — that distinction matters when choosing among catalog tools. Default output is 2–4 pages; very large research compilations spanning dozens of sources may need manual structural review after generation.
- Составление договоров и оценка рисков — Contract Drafter is a Claude Code skill that produces a complete contract or agreement as a Word document, paired with a pre-signing risk checklist. All output is for reference only and must be reviewed by a licensed lawyer before signing.
Reach for it when you need to draft a services or outsourcing agreement quickly, put together a memorandum of cooperation, scan an existing agreement for one-sided or risky clauses, or get a structured starting point for negotiations with a counterparty.
The skill first collects all required parameters — parties, subject matter and scope, amounts and payment schedule, deadlines as absolute dates, acceptance criteria, liability for breach, confidentiality, intellectual property ownership, termination conditions, and dispute resolution venue. It then generates a Word document with a standard structure: preamble with party details, recitals, numbered body articles, and a signature block. Every monetary amount appears in figures and in words; every obligation is paired with a consequence for breach. The document closes with a pre-signing checklist that flags clauses leaning against the user or left open for negotiation, explaining the risk for each.
The direction is drafting reference documents, not legal analysis of applicable law.
- oh-my-claudecode — справочник агентов и оркестрации — omc-reference is a Claude Code skill that serves as the built-in oh-my-claudecode reference: a catalog of 19 agents with model routing (haiku/sonnet/opus), tool documentation, a skills registry, and a structured commit protocol. It auto-loads when delegating to agents, using OMC tools, orchestrating teams, or invoking skills — keeping CLAUDE.md lean while detailed reference stays available on demand. The skill covers every agent role (explore, analyst, planner, architect, debugger, executor, verifier, and more), state and notepad commands, project memory, LSP and AST tools, and a five-stage team pipeline. The skills registry includes both workflow skills (autopilot, ralph, ultrawork, ralplan, deep-interview, ai-slop-cleaner) and utility skills. Built for developers who need centralized orchestration documentation without bloating the session prompt.
- Управление контекстом проекта через документацию — context-driven-development is a Claude Code skill that creates and maintains project context artifacts — product.md, tech-stack.md, workflow.md, and tracks.md — inside a conductor/ directory. It can scaffold greenfield projects from scratch, extract structured context from existing codebases, validate artifact consistency before implementation begins, and keep documents in sync as the project evolves. Teams use it for onboarding, defining development workflows, managing tech stack decisions, and ensuring that every AI-assisted session starts from a shared, up-to-date understanding of the project rather than guesswork or stale assumptions.
- Персистентная база знаний между сессиями — wiki is a Claude Code skill that implements a persistent markdown knowledge base compounding across sessions, inspired by Karpathy's LLM Wiki concept. It exposes six core operations: `wiki_ingest` (process knowledge into one or more pages), `wiki_query` (keyword and tag search with LLM-synthesized answers and citations), `wiki_lint` (health checks for orphan pages, stale content, broken cross-references, and structural contradictions), plus `wiki_add`, `wiki_read`, and `wiki_delete`. Pages are stored as YAML-frontmatter markdown files under `.omc/wiki/`, organized into categories such as architecture, decision, pattern, debugging, and session-log. Cross-references use `[[page-name]]` wiki-link syntax, and at session end significant discoveries are automatically captured as session-log pages — all without vector embeddings, relying solely on keyword and tag matching.
- Система памяти для писателей-фикшн — writer-memory is a Claude Code skill that provides an agentic memory system for fiction writers, persisting character profiles, emotional arcs, relationships, scenes, and themes across Claude sessions. All data is stored in a single `.writer-memory/memory.json` file designed for git-friendly collaboration. The skill exposes over 15 commands — including `init`, `char add/update`, `rel add/update`, `validate`, `synopsis`, and `export` — covering everything from tracking a character's forbidden words (taboo) and speech formality level to checking whether a line of dialogue fits that character's established tone. The `synopsis` command generates an emotion-focused story summary structured around five narrative elements. Korean-language workflows receive first-class support, with field labels such as 감정궤도, 대사톤, and 말투 레벨, making this skill especially suited for authors writing in Korean storytelling traditions.
- Подготовка к сертификации CMMC Level 2 — achieving-cmmc-level-2-compliance is a Claude Code skill that guides defense-contractor organizations through CMMC Level 2 certification: scoping CUI and FCI boundaries, implementing all 110 NIST SP 800-171 Rev 2 security requirements across 14 families, computing an SPRS score using the DoD Assessment Methodology, and building documentation for a C3PAO third-party assessment. The skill covers full workflow — classifying assets into scoping categories (CUI Assets, Security Protection Assets, Contractor Risk Managed Assets), authoring a System Security Plan, and managing a compliant POA&M under 32 CFR Part 170 constraints, including the minimum score of 88 for Conditional status and the 180-day closure deadline. Designed for Defense Industrial Base teams operating under DFARS 252.204-7012/7019/7020/7021 contracts that store or process Controlled Unclassified Information.
- Написание bug bounty репортов — report-writing is a Claude Code skill that guides bug bounty hunters through writing platform-ready reports for HackerOne, Bugcrowd, Intigriti, and Immunefi using an impact-first, human-tone approach. It provides dedicated report templates for each platform, a title formula, an impact statement formula, a CVSS 3.1 scoring guide, downgrade counter arguments, and a pre-submit checklist. The core rule is strict: no speculative language like "could potentially" or "may allow" — every claim must be proved or dropped. Each session ends with a complete findings/<target>-<bug-class>/ folder on disk containing the report draft, submission-notes.md, and an evidence/ directory, so nothing is lost in terminal scrollback. Ideal for security researchers aiming to increase report acceptance rates across major bug bounty programs.
- Локальная база знаний для AI-агентов — dbs-knowledge is a Claude Code skill that turns a local folder into a knowledge base that AI agents can reliably search, update, and maintain. It covers four core operations: build — create a `SOURCE_OF_TRUTH.md` navigation file along with agent entry rules (`AGENTS.md`, `CLAUDE.md`); store — place new materials in the right location while resolving naming conflicts and version relationships; use — locate source files and answer questions with traceable references; and audit — detect broken paths, version conflicts, scattered files, and duplicate content. No database, vector store, embedding service, or third-party upload is required. The skill suits anyone who wants to organize a local document archive, enable AI-driven file retrieval, or keep a working folder structured without dealing with technical configuration details.
- Многослойная база знаний для личных и бизнес-решений — dbs-decision is a Claude Code skill that builds a local decision knowledge base for tracking long-term decisions across business, relationships, health, career, learning, and investments. It organises information into four strict layers: facts (`01_事实/`), patterns (`02_规律/`), state snapshots (`03_定格/`), and open questions (`04_待解/`), each with its own rules for writing and editing. The skill operates in five modes — project initialisation, current-state update, decision filing, result backfill, and periodic snapshot — and tags every entry with source markers such as `[AI 推测]`, `[结果回填]`, and `[修正]` to keep facts, judgements, and outcomes clearly separated. A privacy mode can be enabled at init to replace real names with aliases across all files. It is designed for anyone managing complex, multi-stage processes who needs to revisit past decisions months later with full context intact.
- Сводный Markdown-отчёт из диагностических архивов — dbs-report is a Claude Code skill that merges multiple diagnostic snapshots saved by dbs-save into a single, shareable Markdown report suitable for clients, partners, or external consultants. It reads all archive files from `sessions/{project}/`, sorts them chronologically, deduplicates content, and organizes the output into six sections: evolution of the user's stated problem, confirmed conclusions, rejected directions, open questions, recommended next steps, and a full archive index. The skill supports `/dbs-report`, `/dbs-report --since YYYY-MM-DD`, and `/dbs-report --slug <project>` commands. Every generated report is written to `reports/{project}/` with a unique timestamp and never overwrites an existing file. It is designed for consultants and business analysts who need to consolidate incremental diagnostic sessions into a traceable, structured deliverable.
- Атомарная структуризация базы знаний и контента — dbs-content-system is a Claude Code skill that turns accumulated drafts, articles, case studies, course scripts, and social posts into a structured, reusable content engineering system designed to keep growing over time. It breaks content down into atomic units of five types — QST, CON, OPI, CAS, and SOL — then builds topic maps, tracks relationships, deduplicates entries, and supports Obsidian graph visualization. The skill ships with 10 Node.js tools covering scaffold initialization, unit draft generation, relationship mapping, duplicate detection, and assembly of new topic drafts, plus 7 templates and a complete ruleset. It is designed for situations where a large body of material already exists (50+ files or 80,000+ words) and the goal is a reusable content system rather than editing a single piece.
- Написание баг-репортов для bug bounty платформ — report-writing is a Claude Code skill that structures bug bounty reports for HackerOne, Bugcrowd, Intigriti, and Immunefi using an impact-first, human-tone approach. It includes platform-specific report templates, a title formula, an impact statement formula, CVSS 3.1 scoring guidance, severity downgrade counters, and a pre-submit checklist. The skill enforces a strict no-theoretical-language rule — every claim must be proven, never hedged with "could potentially." Each session persists the report draft, submission notes, and evidence under a findings/ folder on disk. Designed for bug hunters who have already validated a finding and need to write a clear, triager-ready submission.
- Визуализация объяснений ИИ-агента в HTML — visual-explainer is a skill for an AI agent that turns complex terminal output into tidy HTML pages you actually want to read. Reach for it when you ask an agent to explain a system architecture, review a diff, or compare requirements against a plan, and instead of ASCII art and box-drawing tables you want proper visualization: the agent generates a self-contained, styled HTML page and opens it in your browser. Its focus is presenting an AI agent's explanations and diagrams as readable HTML pages, not a general diagram editor (drawio) or a presentation generator: the value is getting clear, styled explanations from the agent instead of text pseudo-graphics. It suits people who work with AI agents in the terminal and want architecture, diff, and plan explanations to look clear.
- md2html — Markdown-документы в HTML-страницы — md2html is a portable skill for Claude Code, Codex, Antigravity and other AI agents that turns long Markdown documents into a single self-contained HTML page with a sidebar table of contents, Mermaid diagrams, step timelines, callouts, comparison cards and a light/dark Claude-orange theme. Reach for it when a plan, spec, system design, RFC, runbook, postmortem or set of notes from your AI is hard to read in a terminal and you need a readable page you can send, open offline or print to PDF. Run it with /md2html plan.md (an --out option sets the output path): the agent reads template.html and components.md, analyses the structure and decides what becomes a diagram, step cards, a pros-cons box or a collapsible panel. It is an analyzer, not a plain converter. The interface language follows the document: English, Vietnamese, Chinese, Japanese, Korean, Spanish, French and German are built in, and the agent translates the rest; right-to-left languages are LTR-only for now. Install by cloning the repository into the skills folder; no build step, server, Node or Python is needed. The only network request is the Mermaid CDN, which can be inlined. The direction is Markdown to HTML. Not for dynamic pages or CMS publishing.
- Nextcloud MCP — ИИ-доступ к вашему Nextcloud — Nextcloud MCP Server (repository mcp-nextcloud) is an MCP server that connects AI assistants (Claude, GPT, Gemini and others) to your Nextcloud: notes, calendar, contacts, tables and WebDAV files, 30 tools in all. It is a TypeScript rewrite of the Python project cbcoutinho/nextcloud-mcp-server. Reach for it when you want an AI to find a Nextcloud file by name, content or metadata without knowing the exact path; when you want to create a note or calendar event from a chat sentence; when an agent must read and write files on your cloud; or when you need to add rows to Nextcloud Tables. Tools: Notes (create, search, update, append, delete), Calendar over CalDAV, Contacts over CardDAV (address books and contacts), Tables (schema plus create, read, update and delete rows), and WebDAV (directory listing, read, write, mkdir, delete and unified search with filters and ranking). It runs on your own VPS or through Smithery, and a hosted endpoint is available at https://mcp.techmavie.digital/nextcloud/mcp. For multiple users there is the MCP Key Service: instead of Nextcloud credentials in the URL you use a key of the form usr_..., and credentials are resolved server-side. Licensed under AGPL-3.0. You need access to a Nextcloud instance; writes and deletes run as your account, so review what the AI does.
- Создание базы знаний с источниками для AI-агентов — nvk/llm-wiki is an open-source tool that builds LLM-compiled knowledge bases and plugs into any AI agent as a first-class plugin. Reach for it when you need a traceable, source-backed knowledge base rather than a one-off model answer; when you're taking a rough idea through research and want to promote it into a structured delivery project; when the output must survive as a reusable artifact and not disappear into a chat thread; or when you're running thesis-driven research and need the wiki to confirm or challenge your starting claim. Under the hood it runs parallel multi-agent research, thesis and collector modes, URL ingestion, session memory, wiki compilation, audits, variable-depth querying, and knowledge-checkpoint exports. Built for Claude Code (native plugin via `claude plugin install wiki@llm-wiki`), OpenAI Codex, OpenCode, and portable agents; stores data in an Obsidian-compatible format. The direction is assembling a knowledge base from scratch around a specific question — not indexing an existing document corpus (use a dedicated RAG pipeline for that). Wiki quality depends on the quality of sources the agents find, so fact-checking remains the user's responsibility. Works well for research and product teams building with AI agents; not a drop-in replacement for a governed enterprise knowledge-management system without additional editorial oversight.
FAQ
How do I keep documentation up to date automatically?
The practical route is not to generate the whole document but to record in the skill which sections are derived from code, and regenerate only those when the relevant files change. Sections that explain reasoning and decisions stay human-written — they cannot be derived from code at all.
How is this different from generators like Swagger?
A generator extracts structure: endpoints, types, parameters. A skill supplies what the code does not contain — why the endpoint exists, which scenario it serves, and what people typically get wrong when calling it. They complement each other rather than compete.
All Claude Code skills