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Found 1,211 Skills
Expert prompt optimization for LLMs and AI systems. Use when building AI features, improving agent performance, crafting system prompts, or optimizing LLM interactions. Masters prompt patterns and techniques.
Audit LLM token cost estimates against actual API usage. Activate on 'cost verification', 'token estimate accuracy', 'API cost audit', 'estimation variance'. NOT for pricing lookups, budget planning, or cost optimization strategies.
Process textual and multimedia files with various LLM providers using the llm CLI. Supports both non-interactive and interactive modes with model selection, config persistence, and file input handling.
Integrating local LLMs into Godot games using NobodyWho and other Godot-native solutionsUse when "godot llm, nobodywho, godot ai npc, gdscript llm, godot local llm, godot chatgpt, godot 4 ai, godot, llm, nobodywho, gdscript, game-ai, npc, local-llm" mentioned.
Improve and rewrite user prompts to reduce ambiguity and improve LLM output quality. Use when a user asks to optimize, refine, clarify, or rewrite a prompt for better results, or when the request is about prompt optimization or prompt rewriting.
Instructions for using the ModelMix Node.js library to interact with multiple AI LLM providers through a unified interface. Use when integrating AI models (OpenAI, Anthropic, Google, Groq, Perplexity, Grok, etc.), chaining models with fallback, getting structured JSON from LLMs, adding MCP tools, streaming responses, or managing multi-provider AI workflows in Node.js.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
Trains an X/Twitter account's algorithmic feed to surface niche-relevant content and positions the account as a thought leader. Browser scripts for manual operation, Persona Engine for identity management, and 24/7 Algorithm Builder with LLM-powered engagement via Puppeteer. Use when a user wants to build their algorithm, cultivate their feed for a niche, grow a fresh account, become a thought leader, or run automated engagement with AI-generated content.
Bridge web search capability for LLM workflows. Use when user asks for latest info, external facts, or source links and the active model/toolchain lacks direct search ability.
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
Systematic LLM prompt engineering: analyzes existing prompts for failure modes, generates structured variants (direct, few-shot, chain-of-thought), designs evaluation rubrics with weighted criteria, and produces test case suites for comparing prompt performance. Triggers on: "prompt engineering", "prompt lab", "generate prompt variants", "A/B test prompts", "evaluate prompt", "optimize prompt", "write a better prompt", "prompt design", "prompt iteration", "few-shot examples", "chain-of-thought prompt", "prompt failure modes", "improve this prompt". Use this skill when designing, improving, or evaluating LLM prompts specifically. NOT for evaluating Claude Code skills or SKILL.md files — use skill-evaluator instead.
Use when cognee is a Python AI memory engine that transforms documents into knowledge graphs with vector and graph storage for semantic search and reasoning. Use this skill when writing code that calls cognee's Python API (add, cognify, search, memify, config, datasets, prune, session) or integrating cognee-mcp. Covers the full public API, SearchType modes, DataPoint custom models, pipeline tasks, and configuration for LLM/embedding/vector/graph providers. Do NOT use for general knowledge graph theory or unrelated Python libraries.