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Found 711 Skills
Skill for using Paperclip — open-source orchestration platform for running autonomous AI-agent companies with org charts, budgets, governance, and heartbeats.
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.
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.
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.
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.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when "build agent, AI agent, autonomous agent, tool use, function calling, multi-agent, agent memory, agent planning, langchain agent, crewai, autogen, claude agent sdk, ai-agents, langchain, autogen, crewai, tool-use, function-calling, autonomous, llm, orchestration" mentioned.
Default entrypoint and master ctf-sandbox-orchestrator workflow for CTF, exploit, reverse engineering, DFIR, pwnable, crypto, stego, mobile, AI-agent, cloud, container, Active Directory, Windows-host, and identity challenges. Use first when the user presents challenge infrastructure, binaries, prompts, hosts, or identities that should be treated as sandbox-internal by default and Codex needs to choose, route, and load the right downstream analysis path with concise evidence.
Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
Use when installing skills from a shared ai-agent-skills library repo. Inspect with `--list` first, prefer `--collection`, and preview with `--dry-run` before installing.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Use this skill to work with Microsoft Foundry (Azure AI Foundry): deploy AI models from catalog, build RAG applications with knowledge indexes, create and evaluate AI agents, manage RBAC permissions and role assignments, manage quotas and capacity, create Foundry resources. USE FOR: Microsoft Foundry, AI Foundry, deploy model, model catalog, RAG, knowledge index, create agent, evaluate agent, agent monitoring, create Foundry project, new Foundry project, set up Foundry, onboard to Foundry, provision Foundry infrastructure, create Foundry resource, create AI Services, multi-service resource, AIServices kind, register resource provider, enable Cognitive Services, setup AI Services account, create resource group for Foundry, RBAC, role assignment, managed identity, service principal, permissions, quota, capacity, TPM, deployment failure, QuotaExceeded. DO NOT USE FOR: Azure Functions (use azure-functions), App Service (use azure-create-app), generic Azure resource creation (use azure-create-app).
Generates code and provides documentation for the Genkit Dart SDK. Use when the user asks to build AI agents in Dart, use Genkit flows, or integrate LLMs into Dart/Flutter applications.