Maintains docs/ in the Karpathy-style LLM-wiki standard. Generates docs/llms.txt (flat machine-readable index conforming to llmstxt.org), validates broken links, enforces a minimum front-matter in new .md files (title, status, updated, related), groups orphan docs. Use when the user says "update docs index", "generate llms.txt", "audit docs", "organize wiki", "cure documentation", or after adding/renaming a file in docs/.
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npx claudepluginhub andersonlimahw/lemon-ai-hub --plugin llm-wiki-curatorGenerate a complete, production-ready CLI in Bun from a SPEC, OpenAPI contract, JSON Schema, or natural-language description. Outputs a runnable binary with commander-style arg parsing, autocomplete, man page, and token-aware output formatting.
JavaScript/TypeScript bundle size analysis, tree-shaking audit, and code splitting recommendations. Identifies heavy dependencies, duplicate packages, unused imports, and suggests dynamic imports and lazy loading. Use when bundle is too large, Lighthouse score is low, or user wants to optimize web app loading performance.
Wrapper that bridges the AI harness to external CLIs — captures help output, documents commands, saves input/output token metrics, and provides a standardized interface so the harness invokes CLIs without context bloat.
Async and concurrency pattern advisor for JavaScript/TypeScript, Python, Go, and Rust. Identifies race conditions, missing error handling in promise chains, N+1 async anti-patterns, unbounded parallelism, and missing cancellation. Recommends correct patterns: Promise.all, p-limit, AbortController, AsyncLocalStorage, worker threads. Use when async code has bugs, timeouts, or memory leaks.
Load testing setup, execution, and analysis with k6, Artillery, or Locust. Generates test scripts, defines VU ramp-up scenarios, interprets p99 latency and error rate results, and suggests infrastructure fixes. Use when user wants to load test an API, check throughput limits, validate SLO headroom, or diagnose performance under traffic.
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).