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Found 1,573 Skills
Build structured hierarchical memory systems for LLM agents using GAM (General Agentic Memory) with support for text, video, and agent trajectories
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
Quickly test and compare LLM models via OpenRouter. Find the fastest/cheapest model, compare response quality. Trigger words: openrouter, test model, compare models, find fastest model, find cheapest model
Consult an advisory council of three AI personas — Cato (skeptic), Ada (optimist), Marcus (pragmatist) — backed by different frontier LLM agents (Gemini, Claude, Codex). Each persona runs as a separate agent process with full repo context and returns independent feedback. Use when the user says "/council", asks for a second opinion, wants feedback on code changes, needs a premortem, wants to pressure-test a decision, or asks "what do you think about this approach?" Claude may also proactively suggest consulting the council before major architectural decisions, risky deploys, or ambiguous trade-offs (but should ask for user approval first).
Backend development agent for Resume Matcher. Handles FastAPI endpoints, Pydantic schemas, TinyDB operations, LiteLLM integration, and Python service logic. Use when creating or modifying backend code.
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
Use when building a custom provider integration on top of @prefactor/core so your app can instrument agent, llm, and tool workflows without relying on a prebuilt adapter package.
Creates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
Execute deterministic, event-sourced security audits using ESAA-Security's LLM-based agent architecture with 95 checks across 16 security domains