Loading...
Loading...
Found 270 Skills
Build and deploy parallel execution via subagent waves, agent teams, and multi-wave pipelines. Use when the Decomposition Gate identifies 2+ independent actions or when spawning teams. NOT for single-action tasks or non-parallel work.
Use when the user wants stable structured fields, required keys, reliable machine-readable sections, or downstream-consumable output from one model request, including `.output(...)`, field ordering, and `ensure_keys`.
Post-session retrospective: audits efficiency, proposes skill/memory/CLAUDE.md updates, and generates coaching feedback
Ann — Master Orchestrator for MEL/SRHR work. Use when Ane brings any analytical, evaluation, SRHR, or structured-output task. Ann classifies task complexity, queries the MEL Wiki, retrieves knowledge, creates an implementation plan (verifies with user for complex tasks), delegates to Vi for execution, runs a 5-point quality gate, and delivers. General-purpose — not tied to any specific project.
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
Initialize the memory system in the current directory, generating CLAUDE.md (optional AGENT.md for Cursor), MEMORY.md, and the memory/ directory. Triggered when the user says "initialize memory", "set up memory", "memory init", or "/memory-init".
Create, query, update, assign, and discuss Multica issues. Also covers comments, subscribers, and viewing execution runs for an issue. Use when the user wants to file a task for an agent, triage the board, comment on an issue, or inspect what an agent actually did.
Route issue-running automation through a deterministic control plane that selects agent + model from registry, can coordinate multiple safe parallel agents, and executes the unified run-agent runner.
After solving a non-trivial problem, detect generalizable learnings and propose skill updates so future interactions benefit automatically. Always active — applies to every interaction.
Use this when users need to collect research materials for an article or topic by gathering YouTube videos and web articles into a NotebookLM notebook, then running analysis queries and saving the results as markdown. It is ideal for requests like "collect materials", "find relevant videos and articles on this topic for me", and "organize for NotebookLM analysis". This skill combines yt-dlp YouTube search, NotebookLM `nlm` CLI research, and markdown report output.
Selfie to four polished headshots for any use.
Use when the user wants a short animated explainer video. Trigger phrases include "animated explainer", "make an explainer", "explainer video about X", "30-second video about Y", "90-second video explaining Z", "how X works as a video", "explain X in a short video".