Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Found or continue a roadmap autopilot one stage per invocation. Default: human merge. --fullauto is invocation-scoped and uses the transient wrapper only after a fresh audit. Triggers: "ship-roadmap", "ship the roadmap", "autopilot this project".
Use when the current Spec Pack must be abandoned/revoked due to a major problem with the requirements, the corresponding `.aisdlc/specs/{branch}` directory and both local and remote branches must be deleted, and a deletion list must be output before performing the deletion with a request for the user's second confirmation.
Keep an agent disciplined while vibe coding: small shippable slices, verify before done, prefer lean skills over long prompts, and separate UI from API layers. Use this skill when scoping work, starting a vibe-coding session, or the agent is thrashing — for any stack. Not for Sarvam API signatures (use sarvam-mcp, translate, speech-to-text, text-to-speech, chat, or voice-agents).
Use when the user asks to align, confirm, or correct the agent's understanding before action; when prior misinterpretation, risky ambiguity, release/version/commit intent, destructive effects, or goal disagreement could cause rework or damage.
Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent readiness", "agent-ready score", "MCP server card", "agent skills index", "markdown for agents", "content signals", "web bot auth", "agent discovery", "RFC 9727", "RFC 8288", "RFC 9728", "SEP-1649", "WebMCP", "ARD", "ai-catalog", "x402", "UCP", "ACP", or wants to make a website discoverable and usable by AI agents.
Emits a dev-tool-agnostic handoff manifest from ready-for-dev stories so an external dev plugin or runner can pick up and execute the work. Use when the user says "generate a handoff", "create handoff manifest", "export stories for dev", "hand off to dev tool", "produce handoff manifest", "ready to hand off", "prepare handoff for external tool", "export ready-for-dev stories", or "create the handoff package". Also trigger when the user asks "what stories are ready for dev?" and wants an exportable artifact rather than a status report. Produces: handoff-manifest.json listing all ready-for-dev stories with id, story file path, status, owned file/module scope, wave/parallel_set, dependencies, acceptance-criteria summary, locked-sections note, and a schemaVersion field. See REFERENCE.md (bundled) for the full manifest schema and adapter notes for git-worktree parallel development and autonomous dependency-graph orchestrators.
Use when the user asks to "generate (test) scenarios", "generate evaluators", "create an evaluator", "create evals", "create a scenario", "write a test scenario", "design a test case", "test my agent", "build eval coverage", "plan a test suite", "create red team tests", "set up test profiles", "configure conditional actions", "build a deterministic test", "design an IVR test", "write a unit test for a voice agent", "build a regression test", "scripted scenario", "structured evaluator", or "run evals". Also for CHANGING existing evaluators — "update an evaluator", "improve my evals", "make these evaluators stricter", "add a DTMF step", "fix the expected outcome", "attach metrics to these" — and for debugging how the testing agent speaks: "why did it read the number as a word", "make it spell digits", "wrong language". Covers evaluator design and review, coverage, test profiles, mock-tool data, conditional actions, and red-team / edge-case practice.
Use natural language to create, query, edit, publish or delete DingTalk digital employees, manage their capabilities and resources, check execution status, save local Profiles, and connect to DSH. Use when the user asks to create or manage digital employees, change personas/roles/response modes, publish or ask about retiring a capability, check execution status or traces, manage digital employee Skills/MCP, convert an existing digital employee to a local Profile, or connect to local DSH. Command prefix: dws dingtalk-tag
This skill should be used for structured feature development with codebase understanding. Triggers on /do command. Provides a 5-phase workflow (Understand, Clarify, Design, Implement, Complete) using codeagent-wrapper to orchestrate code-explorer, code-architect, code-reviewer, and develop agents in parallel.
CodeStable is an AI coding workflow for serious software engineering that keeps humans in the loop. Rather than centering on Agent orchestration, it organizes requirements, architecture, features, issues, and historical decisions to make development driven by Codex and Claude controllable, traceable, and continuously evolvable.
CodeStable is an AI coding workflow for serious software engineering that keeps humans in the loop. Rather than centering on Agent orchestration, it organizes requirements, architecture, features, issues, and historical decisions to make development driven by Codex and Claude controllable, traceable, and continuously evolvable.
Set up the Fiddler Everywhere MCP server connection for AI agent tools (Claude Code, GitHub Copilot CLI, GitHub Copilot in VS Code, Cursor, OpenAI Codex CLI). ALWAYS use this skill when Fiddler MCP tools are not available, you cannot see Fiddler tools, get an authentication error connecting to Fiddler, need to configure Fiddler MCP for the first time, or hear "tool not found" errors when trying to use other Fiddler skills. Calls Fiddler's local autoconfigure API to detect existing config and write the correct settings automatically. The only prerequisite is that Fiddler Everywhere is running.
Implement a complete closed-loop process for handling web editor to-dos via the Axhub AI Extension (Chrome extension) together with the @axhub/genie CLI. Guide users through installing the extension and detecting connection status, discovering online editor clients, reading user annotations and to-do nodes, exporting context screenshots and images, setting edit status (editing/idle/completed/error four-state lifecycle), completing code changes and writing back the final state. Supports communication stability safeguards such as CLI automatic retries, exponential backoff reconnection, tab visibility awareness, and task state persistence. Use when the user has annotated web modification to-dos via Genie Editor and wants AI to handle them.
Index and routing table for this repository's Go skills: maps a task to the skill that owns it, names the secondary skills worth loading alongside, and draws the boundary between skills whose triggers overlap. Use when it is unclear which Go skill applies, when a task spans several concerns at once, when two skills seem to cover the same ground, or when the user asks what Go skills are available or wants a project configured to load them. Trigger examples: "which skill should I use", "what Go skills do you have", "is this concurrency or performance", "set up the Go skills for this repo". Not for: a task that already maps cleanly to one skill — load that skill directly.
Use this skill when the user asks to “create a hedera plugin,” “build a hedera agent kit plugin,” “extend hedera agent kit,” “create custom hedera tools,” “add hedera functionality,” “write a hedera tool,” or “implement hedera tool,” or needs guidance on Hedera Agent Kit plugin architecture, tool definitions, mutation tools, query tools, or parameter schemas using Zod.
Hybrid adaptive memory: Cheatsheet (positive patterns pre-generation) and Immune (negative patterns post-generation) with Hot/Cold tiered auto-learning. Triggers on: "scan for errors", "immune scan", "check output quality", "antibody scan". NOT for PR review (use pr-review) or repo audits (use repo-sentinel).
Use when validating subjective quality criteria that cannot be deterministically tested — applies LLM-based evaluation with structured rubrics for tone, aesthetics, UX feel, documentation quality, and code readability. Triggers: documentation quality check, error message tone review, UX copy evaluation, code readability assessment, design aesthetic review.
Append a structured entry to the project's session log (`docs/LOGS.md`): what was done this session, files touched, decisions taken, and the next step — so the next session (or another person) can pick up the thread without re-reading git history. Run it before `/clear`, before closing your agent session, or at any natural stopping point. Model-agnostic: the skill inherits whatever model and effort your agent session is already using. Triggers: "log this session", "log-session", "write a session log", "journal what we did", "record this session before I clear", "save the session summary".
Design well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.
Use when exposing Spring Boot 3 application capabilities through Model Context Protocol tools, resources, or prompts. Covers Spring AI 1.x tool callback registration, transports, schemas, errors, security, and standalone MCP Java SDK compatibility.
Run `openspec init` to initialize OpenSpec in a project directory, creating the openspec/ folder structure and configuring AI tool integrations. Use when the user says "initialize OpenSpec", "openspec init", or "set up OpenSpec in this project".
Validate that implementation matches change artifacts using `/opsx:verify`, checking completeness, correctness, and coherence. Use when the user says "verify implementation", "check my work", "/opsx:verify", or wants quality validation before archiving.
openydt (Akezhibo parking open platform CLI) shared foundation: profile/credential configuration, multiple environments (test/dev/prod), v2/v3 signatures, response envelope and status/resultCode, exit codes, rate-limit retries, write operation safety rules, and parking-lot experience notes (park-notes). Read this foundation first when using openydt for the first time, configuring/switching profiles, troubleshooting signature/authentication/rate-limit issues, or before performing any write operation; all openydt domain skills should Read it before executing.
A meta-skill for creating or standardizing custom openydt (Akezhibo parking open platform CLI) Skills. Use when the user wants to create a new openydt domain skill, solidify an interface or a multi-step business process into a reusable Skill, or standardize an existing SKILL.md (frontmatter / command table / trigger words / write operation --yes guard). Comparable to Feishu's lark-skill-maker.
OpenSpec-flavored end-to-end Jira bug-fix workflow that persists root cause, behavior change, fix plan, verification, and archive into OpenSpec artifacts (openspec/changes/<name>/, archived into openspec/specs/) instead of leaving them only in chat context or Jira comments. Use when a Jira issue needs long-term behavioral-contract traceability, team review, or auditability. Do NOT use for a quick fix needing no traceability — use jira-fix-workflow instead. Triggers: "opsx-jira-fix", "OpenSpec Jira fix", "规范化修复 Jira", "opsx修复Jira", "Jira OpenSpec 修复", "opsx自动修复Jira", "用OpenSpec修复Jira", "opsx-jira-fix-workflow"; "ai-proxy mode", "AI 代理模式", "切换 ai-proxy" / opsx jira fix, OpenSpec Jira fix workflow, ai-proxy mode, switch to ai-proxy.
Refine a raw research question into a structured deep research task. Clarify requirements through questions and generate structured prompts that meet OpenAI/Google Deep Research standards, fully replacing ChatGPT's question refinement feature. Use this skill when the user raises a research question, needs help defining research scope, or wants to generate structured research prompts.
Repo Updater - Multi-repo synchronization with AI-assisted review orchestration. Parallel sync, agent-sweep for dirty repos, ntm integration, git plumbing. 17K LOC Bash CLI. Use when syncing multiple GitHub repositories, running agent-sweep on uncommitted changes, or orchestrating AI-assisted code review across repos.
Use when options, a recommendation, or an already-chosen approach are on the table and the reasoning under them has not been independently checked — especially when the case rests on cited documentation, remembered behavior, or premises nobody verified. Not for generating alternatives, and not for critiquing a spec, which is /pushback.
Build, review, or harden AI agents, LLM apps, RAG, tool-calling, coding agents, and agentic workflows with secure-by-default controls. Trigger on prompt injection, tool permissions, approvals, AI data handling, RAG poisoning, observability, or safety CI. Do NOT use for non-AI appsec.
Use this skill when the user asks to create or refactor a reusable Codex skill folder, including SKILL.md trigger wording, instruction contracts, rules, references, scripts, assets, and validation checks. Do not use for generic documentation that is not a skill.
The ultimate autonomous dev pipeline. Combines wavybaby (CoVe verification, skill discovery, MCP tooling) + GSD (roadmaps, phases, plans, discovery, state tracking) + Ralph (autonomous loop with circuit breakers). Generates a PRD, equips itself with the best tools, bootstraps a full GSD .planning/ structure, then runs Ralph to autonomously execute each plan with CoVe-verified code until the milestone is complete.
Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization. Applies the RED-GREEN-REFACTOR cycle to process documentation by running a baseline without the skill, writing to address failures, and iterating to close loopholes.
Trekker CLI task tracking for AI coding agents. Covers epics, tasks, subtasks, dependencies, comments, full-text search, history audit log, and kanban-style workflow with local SQLite storage. Use when creating task trackers, managing agent work sessions, tracking epics and dependencies, or implementing local-first issue tracking with Trekker CLI.
Runs project compile, test, and lint commands between implementation and quality review. Gates simplify-and-harden behind machine verification. If checks fail, routes back to implementation with diagnostics for a fix loop. If checks pass, signals ready for the quality pass. Use after any implementation work completes and before simplify-and-harden. Essential for the inner loop's verify step.
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.