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Found 982 Skills
Collect DTMF input and speech from callers using standard gather or AI-powered gather. Build interactive voice menus and AI voice assistants. This skill provides JavaScript SDK examples.
Create and manage video rooms for real-time video communication and conferencing. This skill provides Java SDK examples.
Structured git commit messages following Conventional Commits format for Go projects. Generates well-scoped, atomic commits with clear descriptions. Use when committing changes, writing commit messages, preparing PRs, or reviewing commit history quality. Trigger examples: "commit these changes", "create commit", "commit message", "prepare PR", "squash commits". Do NOT use for changelog generation (use changelog-generator) or code review (use go-code-review).
Guide for adding new AI function examples, for testing specific features against the actual provider APIs.
Creates high-quality Claude Code and Cowork skills using evidence-based principles: expert vocabulary payloads for knowledge routing, dual-register descriptions for reliable triggering, named anti-pattern watchlists for steering past the distribution center, and progressive disclosure architecture for context efficiency. Produces SKILL.md files with structured behavioral instructions, canonical examples, and bundled references. Use this skill when the user wants to create a skill, build a custom capability, make a reusable prompt template, or says "I want Claude to always do X." Also triggers when Mission Planner or Agent Creator need to create a domain skill JIT. Works for any domain. Do NOT use for creating agent definitions (use Agent Creator) or team composition (use Mission Planner).
This skill should be used when a user wants to create, draft, or plan a GitHub Epic issue — for example "write an epic", "I want to define a new initiative", "scope out this strategic project", "turn this idea into an epic", "plan work that spans multiple features", or "start from a bounded context". Also use when the user asks to define domain outcomes, capture a large initiative before breaking it into features, or describe work in terms of business goals rather than technical tasks.
This skill should be used when a developer or QA engineer wants to report a bug, create a bug ticket, document a test failure, log a defect, file an issue found during a QA session, or report something that is broken — for example "report a bug", "create a bug ticket", "I found a defect", "something is broken in task
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Use this skill when the user asks to call an authenticated HTTP API (for example "call the GitHub/OpenAI/Slack API", "hit an endpoint that needs a bearer token") and the `sesame` CLI is already installed on this device. The agent invokes `sesame request`, which forwards the HTTP call through the user's own broker and attaches the auth header server-side. The skill does not install software, does not read credentials from the environment, and runs shell only within the fixed `sesame` subcommand surface (`request`, `status`, `hostnames`, `login`, `refresh`). Skip for unauthenticated public endpoints, localhost services, or when the user has already exported a token in the environment for direct use.
Ultra-compressed response mode. Cuts token usage by dropping articles, filler, pleasantries, and hedging. Uses symbols for relationships. Technical terms and code blocks remain exact and uncompressed. Use when user says "save tokens", "RTU mode", "compress", or "be brief".
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.