By dan323
Generate and maintain project documentation: auto-update CHANGELOG.md from git history with Keep a Changelog formatting (supports monorepos), and create or improve root README.md with a linked /docs directory using ASCII or Mermaid diagrams.
Generate or update CHANGELOG.md files by reading git history. Use this skill whenever the user wants to document what changed in their project — whether they say "create a changelog", "update the changelog", "write release notes", "what's new since v1.2", "document changes for this release", "prep the changelog before we ship", or anything else about recording or summarizing code changes for a version or release. Also use it when the user asks to reformat an existing CHANGELOG to Keep a Changelog format. Works for single repos and monorepos. Always prefer this skill over improvising — it handles deduplication, categorization, and format detection correctly.
Create or improve project documentation. Use when the user asks to "document the project", "create docs", "improve documentation", "write a README", "add a getting started guide", or mentions that documentation is missing or outdated. Produces a minimal root README.md and a /docs directory with linked pages. All diagrams use ASCII or Mermaid.
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npx claudepluginhub dan323/easier-life-skills --plugin docsAnalyze and improve a CV and LinkedIn profile (driven by the official LinkedIn data export, not live scraping) with stronger language, quantified achievements, and aligned keywords
Read tasks from a YAML file (unified or legacy two-file format), implement one per run via an agent, and open a PR
Code-quality audit bundle — find unused code, find breaking REST API changes, and audit logging quality. All three skills are read-only and produce prioritised reports.
Run multi-step skill workflows declared in workflow.yaml — sequential execution, ${{ … }} interpolation between steps, JSON-or-stdout output capture.
Suggest the 5 most valuable next features or improvements for a project
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Build and maintain an LLM-curated personal knowledge base in your project — Andrej Karpathy's LLM Wiki pattern, designed to scale to thousands of pages without becoming a context bottleneck. Now with an optional compiled graph layer for typed, provenance-backed relationships.
Connect to Atlassian products including Jira and Confluence. Search and create issues, access documentation, manage sprints, and integrate your development workflow with Atlassian's collaboration tools.
AI-powered wiki generator for code repositories. Generates comprehensive, Mermaid-rich documentation with dark-mode VitePress sites, onboarding guides, deep research, and source citations. Inspired by OpenDeepWiki and deepwiki-open.
20 modular skills for idiomatic Go — each under 225 lines, backed by 48 reference files, 8 automation scripts (all with --json, --limit, --force), and 4 asset templates. Covers error handling, naming, testing, concurrency, interfaces, generics, documentation, logging, performance, and more. Activates automatically with progressive disclosure and conditional cross-references.
Claude + Obsidian knowledge companion. Sets up a persistent, compounding wiki vault (Karpathy's LLM Wiki pattern). v1.7 "Compound Vault" + v1.8 methodology modes close 5 of 5 priority gaps from the May 2026 compass artifact. Ships: substrate alignment with kepano/obsidian-skills, default Obsidian CLI transport, hybrid retrieval (contextual prefix + BM25 + cosine rerank per Anthropic's Sept 2024 research), per-file advisory locking for multi-writer safety, pre-commit verifier agent, AND methodology modes (LYT / PARA / Zettelkasten / Generic) for first-class organizational support no other Claude+Obsidian competitor offers. v1.7.x audit closure: every BLOCKER + HIGH + MEDIUM + LOW finding from the v1.7.0 audit is CLOSED or DEFERRED-with-rationale. Optional DragonScale Memory extension (log folds, deterministic addresses, semantic tiling lint, boundary-first autoresearch).