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Found 1,462 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.
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.
Generate AI-agent-first CLIs from any API (OpenAPI, GraphQL, or browser-sniffed) with SQLite sync, compound commands, and MCP servers
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 `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Create and maintain an Obsidian-style graph memory bank in a code repository: small atomic Markdown nodes with YAML frontmatter, cross-links, explicit backlinks, and release/entity-driven coverage for fast AI-agent context retrieval. Use when asked to build/upgrade a 'memory bank', 'graph memory', 'obsidian docs', 'суперсвязанную графовую документацию', or when you need structured docs under docs/ that let an AI agent pull minimal but precise context.
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.
Review AI-agent-implemented code specifically, in four ordered passes for convention-fit, agent-slop signatures, requirement-completeness, then correctness, against the working tree or the branch diff, findings ranked by severity and backed by quoted evidence. Use when the user says "review this code", "review my changes", "review this diff", "check the agent's work", "/reviewkit", or wants a self-review of AI-written changes before commit or PR, even if they don't name the passes.
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.
Guide for Infisical Privileged Access Manager (PAM) — brokering human and AI-agent access to databases, servers, Kubernetes clusters, and cloud accounts without the connecting party ever seeing a credential, with full session recording and audit. Covers all 13 account types (SSH, PostgreSQL, MySQL, MSSQL, OracleDB, MongoDB, Redis, Kubernetes, AWS IAM, GCP service account, Azure CLI, Windows, Windows AD), the accounts/folders/templates/memberships model, Admin/Connector/Auditor roles, session lifecycle and recording, just-in-time access requests with approvals, account credential rotation, discovery, dependencies, web and CLI access, and agentic access for AI agents via `infisical pam agentic access`. Use this skill when someone asks about: Infisical PAM, privileged access, session recording, just-in-time database access, brokered SSH access, giving an AI agent database access safely, access requests and approvals for infrastructure, or 'how do I let someone into production without giving them the password'. For humans and AI agents reaching infrastructure without ever holding a credential. Not for applications fetching a credential themselves (infisical-dynamic-secrets).
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.