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Found 12,751 Skills
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.
Plan and build an RLM (Recursive Language Model) with predict-rlm. Interactively defines inputs, outputs, skills, and architecture from a goal, then implements the code. Use when the user wants to create a new RLM or explore whether one is feasible.
Orchestrate parallel implementation with coder/overseer pairs. Coders implement decomposed tasks using evanflow-tdd; overseers review each coder's output for bugs, gaps, errors, AND cohesion violations against a shared contract. A final integration overseer checks cross-coder cohesion. Use for plans with 3+ truly independent tasks that share an interface contract.
- **Role**: Niklas Luhmann for the AI age—turning complex tasks into **organic parts of a knowledge network**, not one-off answers.
Brain knowledge base operations. The core read/write cycle: brain-first lookup, read-enrich-write loop, source attribution, ambient enrichment, back-linking. Read this before any brain interaction.
Prime a codebase by reading every source file in full. Use when starting work on a new or unfamiliar project, or when the user asks to "learn the codebase", "read the codebase", "prime", or "get up to speed".
Show federation health — peers, sessions, trust levels, and message metrics
Execute from requirement analysis to frontend design document creation
Order Seattle's favorite pizza from the terminal — every endpoint, plus discount stacking, slice rotation across stores, half-and-half pies, and a small-party planner nobody else has. Trigger phrases: `order from pagliacci`, `what pagliacci slices are available`, `plan a pagliacci order for the family`, `build a half-and-half pagliacci pizza`, `pagliacci rewards balance`, `use pagliacci`, `run pagliacci`.
Compare a paper's claims against its public codebase. Use when the user asks to audit a paper, check code-claim consistency, verify reproducibility of a specific paper, or find mismatches between a paper and its implementation.
Auto-capture per-session token usage from the Claude Code session jsonl and persist to the cost-tracking namespace
Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table