text-to-speech
Converts text to speech audio using OpenAI TTS API. Use when users request audio versions of text or want responses read aloud.
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Converts text to speech audio using OpenAI TTS API. Use when users request audio versions of text or want responses read aloud.
Crafts Telegram-friendly replies for telclaude sessions, respecting media, heartbeats, and brevity.
Classifies inbound Telegram messages for telclaude as ALLOW/WARN/BLOCK with brief rationale.
Full SDK integration test that runs actual queries through the Claude SDK sandbox. Use after making changes to SDK client code, session management, skill loading, network proxy, voice/TTS, or image generation....
Chat endpoints, embeddings, RAG workflows, vector search
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or...
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hour...
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.
Meta-audit skill for spellbook development. Spawns parallel subagents to factcheck docs, optimize instructions, find token savings, and identify MCP candidates. Produces actionable report.
Use when writing subagent prompts, skill instructions, or any high-stakes task requiring accuracy and truthfulness
Use when starting a session and wanting creative engagement, or when user says '/fun' or asks for a persona
Use when session returns mode.type='tarot' - tarot archetypes collaborate via roundtable dialogue with instruction-engineering embedded
Use when instruction files (skills, prompts, CLAUDE.md) are too long or need token reduction while preserving capability. Triggers: "optimize instructions", "reduce tokens", "compress skill", "make this shorter...
Use on first session in a project, or when user asks for codebase overview. Creates persistent glossary, architecture maps, and decision records to solve agent amnesia.
Use when starting any conversation
Use when: (1) constructing prompts for subagents, (2) invoking the Task tool, or (3) writing/improving skill instructions or any LLM prompts
Persistent iteration wrapper for cognitive reasoning patterns using ralph-loop's Stop hook mechanism. Use when high confidence (>90%) is required, complex multi-pattern orchestration needs iterative refinement,...
Universal ChromaDB integration patterns for semantic search, persistent storage, and pattern matching across all agent types. Use when agents need to store/search large datasets, build knowledge bases, perform...
Enhanced meta-orchestration for selecting and combining reasoning patterns. Now includes 9 methodologies (ToT, BoT, SRC, HE, AR, DR, AT, RTR, NDF) with weighted multi-dimensional selection, feedback loops, unce...
Parallel execution patterns for cognitive reasoning tasks. Use when independent sub-problems can be solved simultaneously, multiple solution approaches need exploration, ensemble confidence is required, or time...
Protocol for cognitive pattern handovers during complex reasoning sessions. Defines .reasoning/ directory structure, handover schemas, and IR-v2 orchestration integration. Use when reasoning sessions require mi...
Self-improving agent architecture using ChromaDB for continuous learning, self-evaluation, and improvement storage. Agents maintain separate memory collections for learned patterns, performance metrics, and sel...
Meta-orchestration guide for choosing optimal reasoning patterns. Analyzes problem characteristics and recommends which cognitive methodology to use - tree-of-thoughts (find best), breadth-of-thought (explore a...
Teaches document writing patterns and templates that agents apply when generating documentation, reports, contracts, guides, and technical writing. Use when creating API docs, user guides, reports, changelogs,...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.