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Found 2,338 Skills
Use Agent Pulse to inspect local AI-agent activity across Hermes, Claude Code, Codex, DeepSeek, OpenClaw, Copilot, Aider, Qwen, OpenCode, Goose, Cursor, Antigravity, and Amp logs. Use when the user asks about AI-agent sessions, tokens, tool/search calls, model usage, estimated cost, budgets, forecasts, health checks, reports, setup diagnosis, web/API/metrics exports, or MCP integration.
Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.
Use this skill when generating AI-agent-friendly documentation for a git repo or directory, answering questions about a codebase from existing docs, or incrementally updating documentation after code changes. Triggers on codedocs:generate, codedocs:ask, codedocs:update, "document this codebase", "generate docs for this repo", "what does this project do", "update the docs after my changes", or any task requiring structured codebase documentation that serves AI agents, developers, and new team members.
Skill for using Paperclip — open-source orchestration platform for running autonomous AI-agent companies with org charts, budgets, governance, and heartbeats.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
AI-agent readiness auditing for project documentation and workflows. Evaluates whether future Claude Code sessions can understand docs, execute workflows literally, and resume work effectively. Use when onboarding AI agents to a project or ensuring context continuity. Includes three specialized agents: context-auditor (AI-readability), workflow-validator (process executability), handoff-checker (session continuity). Use PROACTIVELY before handing off projects to other AI sessions or team members.
Generate AI-agent-first CLIs from any API (OpenAPI, GraphQL, or browser-sniffed) with SQLite sync, compound commands, and MCP servers
Create and maintain an Obsidian-style graph memory bank in a code repository: small atomic Markdown nodes with YAML frontmatter, cross-links, explicit backlinks, and release/entity-driven coverage for fast AI-agent context retrieval. Use when asked to build/upgrade a 'memory bank', 'graph memory', 'obsidian docs', 'суперсвязанную графовую документацию', or when you need structured docs under docs/ that let an AI agent pull minimal but precise context.
Find, compare, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks for a loop, recurring agent workflow, automation cadence, iterative improvement process, an existing Loop Library recommendation, or help turning an outcome into a bounded copy-ready loop through a short question-led design session.
Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases. Triggers on: new/empty project setup for AI agents, AGENTS.md or CLAUDE.md creation, harness engineering questions, making agents work better on a codebase. ALSO triggers when users are frustrated or complaining about agent quality — e.g. 'the agent keeps ignoring conventions', 'it never follows instructions', 'why does it keep doing X', 'the agent is broken' — because poor agent output almost always signals harness gaps, not model problems. Covers: context engineering, architectural constraints, multi-agent coordination, evaluation, long-running agent harness, and diagnosis of agent quality issues.
Discover, find, compare, audit, repair, adapt, craft, run, debrief, save, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop for weak checks or unsafe authority, execute a loop with an evidence receipt, learn from completed runs, save or reuse a project loop, or validate and submit a loop to Loop Library.
Explore and understand Nx workspaces. USE WHEN answering questions about the workspace, projects, or tasks. ALSO USE WHEN an nx command fails or you need to check available targets/configuration before running a task. EXAMPLES: 'What projects are in this workspace?', 'How is project X configured?', 'What depends on library Y?', 'What targets can I run?', 'Cannot find configuration for task', 'debug nx task failure'.