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Found 1,306 Skills
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides REST API (curl) examples.
Use when validating subjective quality criteria that cannot be deterministically tested — applies LLM-based evaluation with structured rubrics for tone, aesthetics, UX feel, documentation quality, and code readability. Triggers: documentation quality check, error message tone review, UX copy evaluation, code readability assessment, design aesthetic review.
Use when implementing speech-to-text, audio transcription, real-time streaming STT, audio intelligence features, or voice AI using AssemblyAI APIs or SDKs. Use when user mentions AssemblyAI, voice agents, transcription, speaker diarization, PII redaction of audio, LLM Gateway for audio understanding, or applying LLMs to transcripts. Also use when building voice agents with LiveKit or Pipecat that need speech-to-text, or when the user is working with any audio/video processing pipeline that could benefit from transcription, even if they don't mention AssemblyAI by name.
Personal wiki at ~/.ultrabrain/ that accumulates knowledge across sessions using an LLM-maintained-wiki pattern. Use when the user asks factual, technical, or decision-oriented questions that may have been previously captured (check index.md before answering), or explicitly asks to capture/記下來/save session content, ingest/整合 raw entries into the wiki, lint/檢查 the vault, or bootstrap a new vault. Skip for small talk, current-file questions, or code-execution requests.
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Supabase Edge Function observability style: tiny provider-neutral OTel-shaped shim, OTLP export config, traces/logs/metrics, and LLM cost metrics.
Self-healing browser automation framework that connects LLM agents directly to Chrome via CDP. Use when the user needs autonomous browser tasks, clean browser verification, Codex or Antigravity browser control, Claude-safe screenshots, adaptive helper code in `agent_helpers.py`, domain skills, or Browser Use Cloud escalation. Triggers on: browser-harness, self-healing browser, llm browser automation, cdp agent, chrome devtools agent, codex browser automation, antigravity browser automation, claude screenshot error, claude image error, agent browser task, browser-use harness, domain skills browser.
Karpathy's LLM Wiki: build/query interlinked markdown KB.
Access 50+ LLM models through AceDataCloud's unified chat APIs. Use when you need OpenAI-compatible chat/responses calls or the newer `/aichat2/conversations` API across GPT, Claude, Gemini, Grok, Kimi, GLM, and DeepSeek models. Supports streaming, multimodal input, and tool calling.
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.