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Found 6,667 Skills
Integrates a Flows/Dune app with the Fusion built-in PAIA agent panel using @cognite/app-sdk. Use this skill whenever a developer wants to: open the agent panel from their app, send the agent a contextual message, let the agent read app state (resources), or let the agent call actions in the app. Triggers: "fusion agent", "PAIA", "agent panel", "sendAgentMessage", "sendAgentLayoutMode", "agent server", "registerAgentServer", "connectToHostApp", "agent integration", "agent sidebar", "app-sdk agent". Always use this skill instead of manually writing agent integration code — it sets up the correct lifecycle, graceful fallback, and recommended file structure.
Create and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Create `AGENTS.md` file for a project. Use when the user wants to set up custom instructions, configure AI coding assistant behavior, or create project-specific coding guidelines for AI agents.
AI agent skill for using deepsec, the agent-powered security vulnerability scanner for large codebases
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For deployment, use agent-platform-deploy.
Map of every agentmemory MCP tool, what each does, and its parameters. Use when choosing which memory tool to call, when a tool name or argument is unclear, or when answering what agentmemory can do via MCP.
Run agency-orchestrator YAML workflows directly in Claude Code / OpenClaw / Cursor — no API key required, using the current session's LLM as the execution engine. Triggered when users provide a .yaml workflow file or request multi-role collaboration to complete a task.
Plan Amazon FBA inventory end to end. Calculates reorder timing and quantity, safety stock, week-by-week days of cover, builds a 12-week PO schedule, applies ASIN-level restock limits and seasonal multipliers, watches the IPI score levers, and surfaces stranded and aged inventory. Use when a user asks about restocking, when to reorder, how much to order, running out of stock, the IPI score, restock limits, ASIN restock limit, days of cover, 90-day forecast, restock schedule, long-term storage fees, aged inventory, or stranded inventory. Trigger phrases. "inventory management", "when to reorder", "restock", "safety stock", "IPI score", "stockout", "aged inventory", "long-term storage", "reorder schedule", "forecast", "days of cover", "ASIN restock limit". Works with zero tools. the user provides sales rate and lead times.
Create and maintain gjkim_instruction.md, the root document for a loop-engineering effort. The document holds only the minimum requirements and confirmed decisions, and deliberately leaves everything else open so that later iterations are not locked into early guesses. Use whenever the user mentions gjkim_instruction.md, a root document or root doc for a loop, loop engineering, starting a long-running agent loop on a goal, or asks to record a requirement or a confirmed decision for such an effort. Also use at the start of a loop iteration to check what is already decided versus still open.
Run fable-mode execution discipline on Claude Opus — the strongest staged run available. Routes the task to the @fable-orchestrator agent (Opus, Write-less), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on opus", "stage this on opus", "deep work mode, opus"). Do NOT use for ordinary single-pass tasks — and prefer fable-sonnet or fable-haiku when the task doesn't need peak reasoning.
Use this skill when a design or idea requires higher confidence, risk reduction, or formal review. This skill orchestrates a structured, sequential multi-agent design review where each agent has a strict, non-overlapping role. It prevents blind spots, false confidence, and premature convergence.