Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Use when you need to execute I2 (implementation execution) in the sdlc-dev Spec Pack: implement in batches with `{FEATURE_DIR}/implementation/plan.md` as the single SSOT, run minimal verification, write back audit information, and report at batch checkpoints; stop immediately on blockers or open questions.
T3 Code multi-harness orchestration: route.mjs selects the harness/model; delegate_task assigns and verifies tasks; automatically switches routes and resumes on rate limits, quota limits, or overload. Invoke only when explicitly named by the user or a skill.
Decompose requirement, design, or brainstorming documents into reviewable task drafts with one function per task and one file per task, then create `agent-team` task packages only after explicit approval. Use when turning a document into tasks, splitting oversized tasks, validating task boundaries, or preparing approved task files for `agent-team task create`.
Task management for AI agents across context windows. Use when agents need to track work, log progress, hand off state, and maintain context across sessions. Includes workflows for single-issue focus, multi-issue work sessions, and structured handoffs. Essential for AI-assisted development where context windows reset between sessions.
Create or revise an implementation specification from a researched software task. Use to turn task-groundwork findings or equivalent sources into a selected in-scope design, explicit behavior, ordered work, and acceptance checks that an implementer can use without conversation history; also use to revise a spec after review.
Primary entry point for the spec-superflow state-machine workflow. Invoke only when the user explicitly requests spec-superflow or an active change contains .spec-superflow.yaml and the user asks to continue that change. Generic proposal, specs, design, task, or contract files are not activation signals.
Work with PostHog Signals scouts: scheduled agents that monitor a project and write reports into the Signals inbox. Use to assign monitoring work, schedule quality scoring, find which scout covers a surface, act on reports, reduce noise, investigate missing findings, or improve the fleet through feedback and calibration. Also covers follow-up checks that verify whether a reported problem stays fixed. Use `authoring-scouts` for edits, `exploring-scouts` for run observability, and `inbox-exploration` for report triage. Trigger on "work with my scouts", "get more out of scouts", "have a scout watch X", "tell me if Y spikes", "score X on a schedule", "measure quality of Y", "what do I do with this scout report", "calibrate/review my scout fleet".
End-of-session persistence of the session's work to Obsidian as a session note per project and the daily note. Use when closing a work session and recording what it changed. Not for mid-session checkpoints, conversation handoffs, standalone project or meeting notes, or repository-wide project context.
Use when writing documentation for agents: creating or editing a skill, modifying AGENTS.md or CLAUDE.md, or writing any rules/context document an agent will read. The focus is not producing consistent text, but making the agent follow the same process every time.
Use when creating new skills, editing existing skills, auditing skill quality, converting skills to markdown conventions, or verifying skills before deployment. Triggers include skill authoring requests, skill review needs, or "the skill doesn't work" complaints.
Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.
Integrate You.com remote MCP server with crewAI agents for web search, AI-powered answers, and content extraction. - MANDATORY TRIGGERS: crewAI MCP, crewai mcp integration, remote MCP servers, You.com with crewAI, MCPServerHTTP, MCPServerAdapter - Use when: developer mentions crewAI MCP integration, needs remote MCP servers, integrating You.com with crewAI
Manage AE Agent and Agent team automations with ae-cli: create and edit schedules, enable or disable, run now, stop or retry executions, delete, inspect history and read execution results. Use for scheduled tasks and unattended recurring Agent work, from a local computer or an AE sandbox
Manage AE AgentSpaces with ae-cli: resolve current workspace context, list or update workspace profiles, transfer files, configure Agent/MCP/Skill/knowledge-base capabilities, manage shared-space members, inspect activities, or scope automations and archived conversations. Use for the new Workspace product; legacy sandbox Workspace sync/model behavior is separate
This skill should be used when the user wants to see all installed plugins, agents, skills, and MCP servers, and also inspect the current repository for local agents, skills, and MCP configuration. Scans the environment and presents a structured catalog of available resources. Works across all AI CLI platforms (Claude Code, GitHub Copilot, Gemini CLI, OpenCode, OpenAI Codex).
Install the AgentHog SDK and wire tracing into the project — init at startup, wrap the primary agent loop, add env vars, verify a first trace lands. Use when the user asks to "set up agenthog", "add agenthog tracing", "instrument with agenthog", "add observability", "wire up tracing", or "add tracing to this app".
This skill should be used when creating, auditing, or maintaining AGENTS.md files and Codex CLI configuration for any project—including initializing AGENTS.md for cross-agent compatibility (Codex, Cursor, Copilot, Devin, Jules, Amp, Gemini CLI), generating config.toml or .rules files, scaffolding .agents/skills/, converting CLAUDE.md to AGENTS.md, or auditing existing agent configs for bloat and staleness. Complementary to codex-orchestrator (which executes subagents; this skill creates the config files they consume).
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".
Composes existing AlterLab skills into multi-agent agentic workflows using current Claude Code orchestration primitives — subagents (including nested subagents), dynamic workflow scripts, agent teams, forks, and the Claude Agent SDK: parallel fan-out, sequential pipelines, judge panels, adversarial verification, and loop-until-clean review cycles. Maps each pattern onto real skills (alterlab-research-pipeline, alterlab-deep-research, alterlab-citation-verifier, alterlab-paper-reviewer, alterlab-peer-review) with copyable delegation prompts, agent-definition frontmatter, workflow-script skeletons, and SDK query() snippets. Use when the request mentions multi-agent, subagents, agent team, dynamic workflow, workflow script, parallel agents, orchestration, pipeline of skills, judge panel, adversarial verification, devil's advocate, loop until clean, chaining skills, or composing skills into a custom workflow. Part of the AlterLab Academic Skills suite.
Shared review framework that every domain reviewer (pci, oracle, gov, edtech, healthcare, mlops, etc.) MUST follow. Defines the output artifact (TM-{slug}.md), mandatory sections, severity scale, verdict format, the workflow scaffold (when-invoked, Step-0 read-inputs, HANDOFF), and the "domain heuristic vs generic check" boundary. Eliminates duplication across the ~30 reviewer prompts.
Route Arduino and embedded-system requests across Arduino IDE, Arduino CLI, PlatformIO, and vendor-specific tools. Use when a request combines firmware, board constraints, electronics, power, networking, calibration, enclosure, upload, debugging, OTA, or maintenance, or when the exact board or toolchain is not yet clear.
Convert an article, tutorial, or prompt pack into focused reusable AgentSkills, one independent capability per skill, with portable instructions, example prompts, working demos, preview screenshots, validation, gallery updates, and a narrow commit. Use when the user asks to turn an article's prompts, tutorial sections, design patterns, interactions, or workflow ideas into complete skills rather than leaving them as prose.
Read-only brainstorming mode for exploring ideas, plans, architecture, debugging approaches, product decisions, research topics, codebase questions, and alternatives without mutating the user's codebase. Use when the user invokes $brainstorm, says "brainstorm", "brainstorming session", "let's think", "explore this", "research only", "read-only", or otherwise asks to discuss before implementation. Remain read-only until the user explicitly asks to implement, edit, write, apply, commit, install, or otherwise change state.
Turn a set of flagged production call logs into evaluator scenarios for a Cekura agent — cluster the failures into reproducible modes and create one scenario per cluster. Use when the user says "create scenarios from failed calls", "build evaluators for prod failures", "turn call logs into scenarios", "simulate the failures we saw in prod", "replay these bad calls as tests", "regression-test the agent on prod issues", or hands over a set of flagged call IDs to harden the agent against. The flagged set normally comes from `cekura-flag-call-log-failures`; if the user hasn't triaged yet, run that skill first. This skill takes the flagged calls as given — it does NOT re-mine or re-triage — clusters by failure mode, drafts one scenario per cluster, and optionally creates them via the scenarios API (or the call-logs → scenarios endpoint). Also supports a single-call fast path: given one call log ID (or an observe URL), reproduce just that call as one evaluator scenario.
Drive a GitHub pull request through ChatGPT Codex's online PR review to a clean merge: trigger a review, poll for the verdict (findings vs clean), fix each finding with local verification, and merge only after TWO consecutive clean reviews on the same commit. Use when iterating a PR against Codex review comments, or when asked to "run the Codex review loop", "handle Codex review findings", or "get this PR clean and merge it". Precondition: the repo has ChatGPT Codex's GitHub integration enabled (bot: chatgpt-codex-connector[bot]); also needs `gh` (authenticated) and `jq`.
Compound Engineering workflow for AI-assisted development. Use when planning features, executing work, reviewing code, or codifying learnings. Follows the Plan → Work → Review → Compound loop where each unit of engineering makes subsequent work easier. Triggers on: plan this feature, implement this, review this code, compound learnings, create implementation plan, systematic development.
Configure .claude/settings.json (permissions, marketplace enrollment, enabledPlugins) and GitHub Actions workflows to use the laurigates/claude-plugins marketplace. Use when onboarding a new project to Claude Code plugins, setting up claude.yml and claude-code-review.yml workflows, adding the laurigates/claude-plugins marketplace to a repo, or merging plugin permissions into existing settings. Natural triggers: "set up claude plugins", "add claude marketplace", "install claude.yml workflow".
Use when the user says "copilot review", "review with copilot", "delegate to copilot", "have copilot fix this", "copilot agent task", "set up copilot", "generate copilot instructions", "bootstrap copilot for this repo", or invokes `/copilot`. Three modes: `review` (run a code review on a PR and route fixes to Copilot via inline `@copilot fix this` comments), `delegate` (create a `gh agent-task`, poll until the coding agent opens a PR, optionally hand off to review), and `setup` (one-time bootstrap that writes `.github/copilot-instructions.md` and the per-role `.github/instructions/*.instructions.md` files). `review` and `delegate` are routine; `setup` is a one-time repo bootstrap loaded on demand from `references/setup.md` only when the user explicitly asks. Do NOT use for plain GitHub PR work — that is `/gh`'s job.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.
