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Found 1,010 Skills
Explains how to use skeeper to keep spec artifacts (SPEC.md, ADRs, RFCs, plan/PRD/TechSpec markdown, custom globs) next to the code they describe without polluting main-repo history. Covers strict hooks, the tracked skeeper.lock file, namespaces, sync/verify/fsck, safe drift workflows with diff/hydrate/reconcile/rescue/update, adopt/untrack/pattern, repair, SKEEPER_SKIP, and the GitHub Action. Use when setting up skeeper, configuring a sidecar, syncing/verifying a lockfile, recovering drift or failed syncs, auditing bypasses, or wiring CI. Do not use for general Git hook questions, repos with no .skeeper.yml and no intent to add one, or editing skeeper internals.
Detect software architecture bad smells, algorithmic complexity hotspots, and anti-patterns in a codebase. Produces a detailed markdown report identifying violations of architectural principles, design patterns, code quality, and performance complexity. Triggers on: smell, code smell, architecture smell, find anti-patterns, detect bad smells, complexity analysis, 代码坏味道, 架构坏味道, 反模式, 找出坏味道, 复杂度分析.
Verify citations and references in scientific documents to detect hallucinated or invalid sources. Extracts DOIs, URLs, arXiv IDs, PubMed IDs, and ISBNs from Markdown, LaTeX, org-mode, and plain text, then validates them using API lookups and web fetches. Use this skill when: - Reviewing AI-generated content for citation accuracy - Validating references in papers, reports, or documentation - Checking if DOIs/URLs resolve to actual papers - Auditing a document for broken or fake citations
An official AI mind map generator developed by ProcessOn, focusing on converting content such as natural language, Markdown, long text, documents, web pages, and image text into professional, clear-structured, well-layered, and editable mind maps with one click. Whether it's article summarization, data organization, document decomposition, knowledge point induction, learning path sorting, or reading notes, paper literature sorting, meeting minutes extraction, work report summary, outline generation, project task decomposition, brainstorming and idea generation, this skill can quickly generate professional mind maps to help users transform scattered content into clear structured knowledge. This skill supports 7 professional graphic layouts including mind maps, logic diagrams, organizational charts, fishbone diagrams, timelines, tree diagrams, and table diagrams, and is deeply integrated with the ProcessOn online collaboration platform. The generated mind maps can be edited online, collaboratively modified, and efficiently reused, suitable for scenarios such as office work, study review, scientific research reading, knowledge management, and scheme planning. Note: This skill is mainly used to generate mind maps and knowledge structure brain maps, and is not applicable to the generation of process or technical charts such as flowcharts, swimlane diagrams, sequence diagrams, system architecture diagrams, ER diagrams, and Mermaid diagrams.
Spawning Plan. Use when user wants to spawn agents, create a team, or coordinate multiple agents. Automatically gathers context, asks team topology questions, outputs clean TEAM PLAN markdown, and gets user approval. 3 steps: context gathering → questions → present plan. **CRITICAL**: MUST NOT SPAWN AGENTS SKIPPING THIS SKILL, USE ALWAYS.
Catalog GitHub starred repositories into a structured Obsidian vault with AI-synthesized summaries, normalized topic taxonomy, graph-optimized wikilinks, and Obsidian Bases (.base) index files for filtered views. Fetches repo metadata and READMEs via gh CLI, classifies repos into categories and normalized topics, generates individual repo notes with frontmatter, and creates hub notes for categories/topics/authors that serve as graph-view connection points. Use this skill when users want to: (1) Catalog or index their GitHub stars into Obsidian (2) Create a searchable knowledge base from starred repos (3) Organize and discover patterns in their GitHub stars (4) Export GitHub stars as structured markdown notes (5) Build a graph of starred repos by topic, language, or author For saving/distilling a specific URL to a note, use kcap instead. For browsing AI tweets, use ai-twitter-radar instead.
Web content extraction via Jina AI Reader API. Three modes: read (URL to markdown), search (web search + full content), ground (fact-checking). Extracts clean content without exposing server IP.
Fetches Apple documentation as Markdown via Sosumi. Use for Apple API reference, Human Interface Guidelines, WWDC transcripts, and external Swift-DocC pages.
Run and interact with KarpathyTalk, an open markdown-based developer social network with GitHub auth, SQLite, and an LLM-friendly JSON/markdown API.
Create a new runbook with guided assistance. A runbook is a structured markdown document that tells a coding agent how to accomplish a complex, multi-step task with evaluation loops and quality gates. Use this skill whenever the user wants to create, build, scaffold, or write a runbook — including 'create runbook', 'new runbook', 'build a runbook', 'make a runbook', 'runbook wizard', 'help me write a runbook', 'I need a runbook for...', 'automate this task with a runbook', or 'turn this into a runbook'. Also trigger when the user describes a multi-step agent task that would benefit from structured evaluation and iteration loops, even if they don't use the word 'runbook' — for example, 'I want to build an automated pipeline that evaluates its own output' or 'create a repeatable process with quality gates'.
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.
Facilitate methodical review of proposals (technical designs, product specs, feature requests). Use when asked to "review this proposal", "give feedback on this doc", "help me review this RFC", or when presented with a document that needs structured feedback. Handles markdown files, GitHub gists/issues/PRs, and other text formats. Chunks proposals intelligently, predicts reviewer reactions, and produces feedback adapted to the proposal's format.