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Found 1,990 Skills
Creates high-quality Claude Code and Cowork skills using evidence-based principles: expert vocabulary payloads for knowledge routing, dual-register descriptions for reliable triggering, named anti-pattern watchlists for steering past the distribution center, and progressive disclosure architecture for context efficiency. Produces SKILL.md files with structured behavioral instructions, canonical examples, and bundled references. Use this skill when the user wants to create a skill, build a custom capability, make a reusable prompt template, or says "I want Claude to always do X." Also triggers when Mission Planner or Agent Creator need to create a domain skill JIT. Works for any domain. Do NOT use for creating agent definitions (use Agent Creator) or team composition (use Mission Planner).
Configure a PreToolUse hook to prevent AI agents from skipping git pre-commit hooks with --no-verify and other bypass flags. Use when setting up Claude Code projects that enforce commit quality gates.
Use when preparing or verifying a host for Moshi remote coding. Trigger this for SSH or preferably Mosh readiness, non-interactive shell PATH issues, tmux defaults, creating a tmux project session rooted at a chosen directory, installing Moshi agent hooks for Claude Code or Codex CLI, or offering the optional `moshi DIR` shell helper.
Ingest any raw text data, conversation logs, chat exports, or unstructured documents into the Obsidian wiki. Use this skill when the user wants to process data that isn't standard documents or Claude history — things like ChatGPT exports, Slack threads, Discord logs, meeting transcripts, journal entries, CSV data, browser bookmarks, email archives, or any raw text dump. Triggers on "ingest this data", "process these logs", "add this export to the wiki", "import my chat history from X". This is the catch-all for any text source not covered by the more specific ingest skills.
Use this skill when the user asks to "evaluate MCP tools", "test tool selection", "improve tool descriptions", "check MCP schema quality", "eval my MCP server", or wants to measure whether Claude uses their MCP tools correctly. Tests tool selection accuracy, analyzes schema quality, and iteratively optimizes descriptions. Companion to build-mcp-server.
A method for iteratively improving text instructions for agents (skills / slash commands / task prompts / CLAUDE.md sections / code generation prompts) by having unbiased executors run them, then evaluating from both perspectives (executor self-report + instruction-side metrics). Repeat until improvement plateaus. Use immediately after creating or significantly revising a prompt or skill, or when you suspect the reason an agent isn't behaving as expected is due to ambiguity in the instructions.
Query AI coding agent usage, costs, and token consumption. Supports Claude Code, Codex CLI, OpenClaw, and OpenCode. Ask about spending, token usage, model costs, session history, API call counts. Actions: check usage, show cost, compare models, list sessions, analyze spending, token breakdown. Time ranges: today, this week, this month, this year, last N days, custom dates.
Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data.
Read, write, and manipulate LAS (Log ASCII Standard) well log files for borehole geophysical and petrophysical data. Use when Claude needs to: (1) Read/parse LAS 1.2 or 2.0 files, (2) Extract well headers or curve data, (3) Convert LAS to DataFrame/CSV/Excel, (4) Create new LAS files from arrays, (5) Modify existing LAS files, (6) Handle problematic or malformed LAS files, (7) Batch process multiple well files.
Use ARIS (Auto-Research-In-Sleep) for autonomous ML research — idea generation, paper review, experiment automation, and cross-model collaboration with Claude Code, Codex, or any LLM agent.
Use when creating a plan using Plan model and enhancing structured design plans in Cursor Plan mode for Java implementations. Use when the user wants to create a plan, design an implementation, structure a development plan, or use plan mode for outside-in TDD, feature implementation, or refactoring work. This should trigger for requests such as Create a plan with Cursor Plan mode; Write a plan with Claude Plan mode; Design an implementation plan; Structure a development plan. Part of cursor-rules-java project
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.