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Found 11,958 Skills
Reference guide for CLI-Anything, which auto-generates production-ready agent-controllable CLI harnesses for any GUI application via a 7-phase.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Build agentic UIs using AG-UI protocol with Pydantic AI (Python backend) and CopilotKit (React/Next.js frontend). Use when creating AI-powered applications that need bidirectional agent-UI communication, shared state between frontend and backend, human-in-the-loop workflows, tool-based generative UI, or predictive state updates. Triggers on requests involving CopilotKit hooks (useCoAgent, useCopilotAction, useCoAgentStateRender), pydantic_ai with ag_ui adapters, or building chat interfaces with backend AI agents.
Create or refresh hierarchical AGENTS.md documentation for Claude Code, Codex/OMX, Gemini, and Antigravity/OMA projects, preserving manual notes while excluding runtime state such as root .omc, .omx, .survey, .codex, and generated build folders.
Token-efficient MCP adapter for Pi coding agent that enables MCP server integration without burning context window
Use when the user asks to "create an evaluator", "create evals", "create a scenario", "write a test scenario", "design a test case", "test my agent", "build eval coverage", "plan a test suite", "create red team tests", "set up test profiles", "configure conditional actions", "write a conditional action evaluator", "build a deterministic test", "design an IVR test", "IVR navigation test", "write a unit test for a voice agent", "build a regression test", "scripted scenario", "scripted voice test", "structured evaluator", "exact flow test", "sequential conditions", "fixed sequence test", or "run evals". Covers individual evaluator design, suite coverage strategy, test profiles, mock-tool data design, conditional actions (deterministic / unit test / regression / IVR navigation flows), and best practices for workflow / red-team / edge-case / deterministic test types.
Free Google Hotels CLI — per-hotel data with deep booking links, agent-native JSON, and local wishlist. No API key. Trigger phrases: `find a hotel in <city>`, `search hotels for <city> <dates>`, `cheapest dates for <city>`, `hotels near <address>`, `compare hotel prices for <city>`, `what hotels are available in <city>`, `save this hotel`, `use hotel-goat`, `run hotel-goat`.
Use OpenClaw MemX for long-term agent memory with self-learning, relationship graphs, and automatic maintenance
Clone a ready-to-run Deepgram demo app and start building on top of it. Use whenever someone wants a quick working demo, needs to prototype with Deepgram, or is starting a new project that uses speech-to-text, text-to-speech, voice agents, audio intelligence, or live streaming. Match the user's language, framework, and desired Deepgram feature to the right starter.
Extend Pydantic AI agents with batteries-included capabilities from pydantic-ai-harness — currently Code Mode, which collapses many tool calls into one sandboxed Python execution. Use when the user mentions pydantic-ai-harness, CodeMode, Monty, code mode, or tool sandboxing, when they want an agent to run agent-written Python, or when a Pydantic AI agent would benefit from orchestrating multiple tool calls in a single sandboxed script.
Loads documents fully into the main agent's context so the agent can answer questions, summarize, or work with that content in subsequent turns. Use whenever the user wants to ingest, read, study, review, absorb, or pull in documents — especially when they say things like "load these docs", "read all of these", "ingest this folder", "pull in these PDFs", "load all docs in X", or paste a list of file paths/URLs and ask you to read them. Handles local files (text, code, markdown, PDFs, notebooks, images), entire folders (recursively), and remote URLs. The skill is single-turn — once the agent reports "DONE", it deactivates until the user invokes it again.
Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory. Do NOT use for: simple questions, short tasks, one-off commands, linting, or code review.