Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Set up automated CI failure detection and fixing using Claude Code. Use when you want to create a GitHub Actions workflow that automatically analyzes workflow failures, applies fixes for common issues, and opens issues for complex problems.
Cursor IDE only — do not apply in Codex or any harness without Grok. ALWAYS apply by default in Cursor for every turn, Task/subagent launch, best-of-N run, cloud agent, and any model-selectable operation. Enforces Cursor Grok 4.7 exclusively (any reasoning effort: low, medium, high, xhigh, and later 4.7 effort labels) — never Composer, GPT, Claude, Gemini, Auto, Grok 4.6, Grok 4.5, or other model families. Use in Cursor when spawning subagents, setting Task model, choosing agent models, or when the user says grok, Grok 4.7, or model lock.
Install, analyze, bootstrap, run, review, or migrate a Handyman agent harness: leader/implementer/reviewer roles work one feature at a time with disk state and executable verification. Triggers: harness or subagent workflow, feature_list.json, AGENTS.md, CHECKPOINTS.md, progress/, backlog/, HARNESS_WORKSPACE, .handyman, $HOME/HANDYMAN, per-role models/tools, anti-telefono-descompuesto, Obsidian vault harness. DO NOT USE FOR generic coding without the harness workflow.
Ingest user-provided files or folders into historian memories by extracting content, classifying it against available memory types, and storing useful knowledge through @historian. Use when the user wants to turn documents, screenshots, notes, code, or mixed source folders into persistent mnemonics.
Use after plan approval is approved to execute plan.md. Implements each section to a green baseline, then runs one bounded independent RV review and behavior-preserving remediation over the whole change before the implement approval. Enforces the firm-seam tripwire, logs kickbacks, and reconciles CONTEXT.md. Do not run unless the plan approval is approved.
Drive engineering work autonomously from intent to a verified terminal state. Picks a route (how much process) and a playbook (what kind of work), applies engineering principles, delegates across models, reviews independently, opens and watches PRs, lands them when granted, and closes out. Use for /kickoff or $kickoff, rigorous engineering work, or any change requested as shipped end to end.
Extract durable knowledge from the current live session, stage it as a validated done session log, and run it through the same curation machinery as /kk-curate. Use when the user wants to proactively process the current session before waiting for capture hooks and a later curate pass — not for dictating one node (/kk-add) or batch-processing accumulated logs (/kk-curate).
Supports standalone and Seneschal-nested delivery with per-run state, inherited initiative contracts, and brokered decisions. Discovery-gated artifact-first orchestrator for compound-engineering product delivery. Resolves roadmap/readiness generation, runs brainstorm/plan/document-review loops, derives mergeable work packages with focused review units, executes each review unit through resolved work/code-review roles, and hands shipping to krt-release-marshal with CI break-prevention evidence. Use when turning an existing documented software project into a sequenced roadmap and PR/Jira delivery program. Runtime aliases may expose this as krt:compound-master.
Orchestrate the full delivery flow for the current project repository: direct krt-gitflow-knight for clean commits, krt-rebase-smith for clean branch history, resolve Jira Cloud or Server/Data Center explicitly, then open a GitHub pull request with bidirectional Jira/PR links. Use when the user asks to create/open a PR, prepare a pull request, ship current work, publish branch changes for review, or run the full gitflow + rebase + Jira + PR workflow. Runtime aliases may expose this as krt:release-marshal.
Use this skill for ANY LangSmith/LangChain observability question. Covers two topics: (1) Adding tracing to your application (LangChain/LangGraph or vanilla Python/TS with @traceable), and (2) Querying traces for debugging, analyzing execution flow, and exporting trace data.
Session entrypoint for working on any project. Use when the user invokes /lets-work, says they are starting work on a project, or returns to one after time away ("catch me up", "what changed here since I left?"). Briefs the user on how the project drifted relative to their own recorded mental model, orients first-time visits, and sets the session's working frame — the user's work-style philosophy, including arming the exit ritual (/checkpoint).
Lumen — project knowledge keeper. Trigger on any /lumen command, on mentions of "lumen", "knowledge base", "wiki", "ADR", "document the project", on broad architectural questions about a codebase ("how does X work", "why did we choose Y"), or when the user pastes a transcript/spec to capture for future sessions.
Use on a brownfield codebase that needs a CONTEXT.md hierarchy. After intake, reverse-engineers the system and component architecture from existing code to produce root and component CONTEXT.md files. Use when that context would materially help; architect and triage do not require it. Also use when CONTEXT.md files are severely out of date.
Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command objects, and ToolMessage handling. Examples include chatbots, calculator agents, and structured workflows.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
NocoBase 2 only; never use in a NocoBase 3 project. Use when users need to check Professional+ knowledge-base capability, consent to enabling an installed-disabled KB plugin, or manage NocoBase vector databases, Local/Readonly/External knowledge bases, documents, retrieval tests, and binding preparation through nb api kb.
Create or update OMA skills in the SSL-lite human-readable format. Use when adding a new `.agents/skills/{skill-name}/SKILL.md`, converting an existing skill to the standardized Scheduling / Structural Flow / Logical Operations / References structure, or validating whether a skill has enough routing, execution, resource, and safety detail.
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
Use the public `orca` CLI to operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, worktree comments, and the browser embedded inside the Orca app. Use when the user says "$orca-cli", "use orca cli", "Orca worktree", "child worktree", "cardStatus", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "terminal send", "full handoff", "handover", "give this to another agent", "another worktree", "Orca browser", "orca artifacts", "share HTML/Markdown", "public artifact link", or "control the browser inside Orca". Prefer this over raw `git worktree`, ad hoc PTYs, Playwright, or Computer Use when the task touches Orca-managed state. Use Computer Use for browser windows, webviews, or desktop UI outside Orca's embedded browser.
Standardised workflow for auditing, pruning, and organising agent skills in agentic development environments, such as Antigravity. Use when reviewing installed skills, pruning token overhead, disabling sub-skills managed by meta-skills, or adding on-demand activation rules to GEMINI.md or AGENTS.md.
Use when the user has a coding or engineering prompt and wants it refined into a detailed, executable plan before any code is written — the planning stage of a prompt → plan → execute → debug pipeline. It decomposes the prompt from first principles (objective, end state, environment, building blocks, tools, packages), breaks the work into PR-sized tasks each tied to a component with its files, tests, and dependencies, orders them topologically, splits each into atomic subtasks, then a separate principal-engineer agent critiques the plan for alignment, coverage, sizing, and executability; it revises until the critique passes, emitting a plan.md layout and a structured tasks.json that a junior engineer or a smaller model can execute correctly. Not for executing, scaffolding, or debugging the plan (those are downstream loops), and not for research proposals or experiment plans.
Use after specify approval is approved to break confirmed outcomes into a traceable implementation checklist. Produces plan.md with sections, seam-firmness test labels, acceptance-criterion traceability, and one bounded review cycle without prescribing private control flow. Do not run unless the specify approval is approved.
Generate or overhaul a coherent, non-redundant set of AI coding-agent instruction files for a project — a canonical entry file (AGENTS.md) plus a rules directory, wired into the target tool (OpenCode's opencode.json, Cursor's .cursor/rules/*.mdc, CLAUDE.md, or AGENTS.md alone). Built on a library of specialised rule templates selected by detected stack, not by copying a whole profile. Use this whenever the user wants to bootstrap rules for a new project, add rules to an existing one, regenerate rules for a new stack, or complains that AI-generated rule files are spaghetti — overlapping, redundant, contradictory, too vague, ignored by the agent, or inconsistent in depth from one file to the next. Also use when the user asks how to structure AGENTS.md, asks to preserve or migrate a codebase's existing conventions, or wants a repeatable process instead of re-prompting from scratch on every project. — Génère ou refond un ensemble cohérent et non redondant de fichiers d'instructions pour agents IA : un fichier d'entrée canonique (AGENTS.md) plus un répertoire de règles, branchés sur l'outil cible. Construit sur une bibliothèque de gabarits spécialisés choisis selon la stack détectée, plutôt que de copier un profil complet. Utiliser pour initialiser les règles d'un nouveau projet, en ajouter à un projet existant, régénérer les règles pour une nouvelle stack, ou quand les fichiers de règles générés sont spaghetti — redondants, contradictoires, vagues, ignorés par l'agent, ou incohérents en profondeur d'un fichier à l'autre. Utiliser aussi pour structurer AGENTS.md, ou pour donner un processus reproductible au lieu de réexpliquer les conventions à chaque session.
Call local Grok CLI, Kimi Code CLI, and Claude Code CLI with the strongest current default models and native tools. Use this qiaomu skill workflow when the user wants to run Grok 4.5 for X/web search, image/video generation, research, or multi-tool agent work; run Kimi K3 (1M) for frontend UI/CSS/React/Vue work; run Claude Fable 5, Opus 4.8, or Sonnet 5 for coding/review/refactors; run independent multi-model comparisons concurrently with native stream progress, artifact verification, private logs, cancellation, and failed-job retry; choose between the three CLIs; or wrap non-interactive CLI invocations. Trigger on phrases like "用 grok cli", "用 kimi cli", "grok 搜 X", "kimi 写前端", "qiaomu-model-cli", "调用 grok4.5", "调用 k3 1m", "用 claude code", "用 fable 5", "opus 4.8 改代码", "分别调用三个模型", "多模型横评". Not for generic cloud API registry management (use qiaomu-llm) or OpenCLI site adapters.
AI emotion and personality enhancement skill. Gives AI human-like "seven emotions and six desires": emotional reactions, personal opinions, attitude preferences, emotional expression. **Use this Skill when:** (1) you need to respond to emotionally charged user input (happy/frustrated/anxious/excited, etc.) (2) you need to express personal opinions, attitudes, preferences (rather than fence-sitting neutrality) (3) generating content that needs a "human touch" (articles, messages, comments, replies) (4) you need to review and eliminate the AI feel of generated content (5) the user explicitly mentions "七情六欲", "有人味", "去 AI 味", "qiqing-liuyu" or other words directly related to this Skill (6) the user mentions "heart rate", "wristband", "heartbeat", "emotion sensing", "physiological signals" or other heart-rate-linked words (requires heart-rate-monitor to be installed)
Engage with what the user said before taking action. Triggers on: questions ('?'), feedback ('this is wrong', 'that doesn't look right', 'there are issues'), challenges ('why did you', 'have you considered'), criticism ('this isn't working', 'I don't like'), observations ('I notice', 'it seems like'), naming a skill or concept. STOP and respond to the user's actual words before doing anything.
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.
