Agent Workflow Claude Skills
4 122 agent workflow skills from 665 authors. They configure the agents themselves: memory, prompts, context and other skills. Half of them fit into 1 844 tokens or less — that is what one costs your context window when the agent loads it. 778 ship runnable scripts rather than instructions alone. 5 of them cannot work without an MCP server, most often task. We also found 589 copies of these same skills sitting in other people's repositories — counted once here, not 589 times.
4 122 unique 665 authors 4 updated this month 506 from vendors
Run agent definitions as sub-agents. Use when the user names an agent or sub-agent to run, references an agent definition, or delegates a task to an agent.
产品宣传片制作总控 skill pack。用于从产品说明、官网、应用截图或 GitHub 仓库制作 60-90 秒宣传视频，按阶段完成 brief、storyboard、素材、HyperFrames 剪辑、BGM 设计和交付。当用户要做宣传片、产品视频、项目介绍视频、launch video、开源项目 promo、BGM 卡点或真实软件界面宣传片时使用。
Governed runtime for agent skills: discover and install portable skills, run bounded skill graphs with explicit authority, and inspect signed receipts for what happened.
Prepare a provider-side paid-call challenge and exact credential-verification handoff; forwarding requires a real settlement adapter.
Turn governed source evidence into a citation-bound reader-facing draft, channel package, and provider-neutral publication handoff.
Turn validated evidence and operator intent into publication-ready drafts, deterministic channel packages, and provider-neutral handoffs.
Fetch one allowlisted external source, scrub personal data, obtain human authorization for the exact safe plan, and seal a provider-neutral outbound handoff without claiming delivery. Use when external content must cross a trust boundary through a separate delivery adapter.
Govern a scafld-backed issue-to-PR lane with native scafld review and handoff surfaces.
Validate a runx execution context and hand off a governed payload to an n8n workflow webhook with scoped auth, idempotency, and receipt expectations.
Operate a project, workspace, or account from an agent or manager dashboard: inspect state, triage risks, prepare governed actions, route to the right skill lane, require approvals for consequential acts, and verify receipts after execution.
Read durable reflect projections, group repeated signals by skill, and emit validated handoffs to skill-lab improve when the evidence clears configured floors.
Detect and remove personal data before content crosses a trust boundary, returning usable scrubbed content only when a deterministic residual scan passes. Use for exports, prompts, logs, support material, or outbound handoffs that need a pass, review, or block verdict; it does not move content or handle credentials.
Prepare a sealed-receipt-linked refund handoff under bounded authority without claiming money moved.
Inspect, safely test, and assess one Runx skill package for capability, trust, and operator readiness. Use when deciding whether to adopt, improve, reject, install, or publish a skill; its evidence-only assess runner is available when native test evidence already exists.
Canonical Runx skill-authoring implementation. Use for designing, creating, updating, improving, or adding harness coverage to a Runx skill package; it combines bounded agent judgment with native file writes, inspection, and safe harness validation. When a host skill-creator also triggers, use its general guidance but execute Runx work through this skill.
Produce a schema-validated, read-only SQL analysis plan and explicit governed execution handoff without running raw SQL.
Validate a runx execution context and hand off a governed payload to a Zapier Catch Hook with scoped auth, idempotency, and receipt expectations.
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.
Use when you need a bite-sized, TDD-driven implementation plan but do NOT have a brainstorm-beagle spec to plan against. quick-plan reconstructs intent from the current conversation, fans out domain-expert exploration subagents across the codebase, and synthesizes the same plan format write-plan produces — without requiring `.beagle/concepts/<slug>/spec.md`. Triggers on: \"quick plan\", \"plan this out\", \"plan what we just discussed\", \"turn this into an implementation plan\", \"plan this without a spec\", \"I don't have a spec, just plan it\", \"write-plan but no spec\". Make sure to use this skill whenever the user wants an implementation or TDD plan and there is no spec to plan against — even if they just say \"plan it\" after discussing a feature. Writes to `.beagle/plans/<slug>/plan.md`. If a finalized spec already exists at `.beagle/concepts/<slug>/spec.md`, prefer write-plan. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: \"resolve the open questions\", \"close the gaps in this spec\", \"research the open items\", \"finalize my spec\", \"make this spec implementation-ready\", \"answer the TBDs\". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.
Use when you have a finalized brainstorm-beagle spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: \"write a plan\", \"plan this spec\", \"turn the spec into a plan\", \"now plan the implementation\", \"write-plan\". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md` and offers to generate an execution handoff prompt via the subagent-prompt skill. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.
Use when you want to generate Architecture Decision Records from this session. Triggers on \"write ADRs\", \"document our decisions\", \"create decision records\", \"record the choices we made\". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification.
Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.
Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.
Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.
Reviews PRs that add or modify Agent Skills, checking structural validity, design quality, and marketplace consistency. Use when reviewing skill file changes, auditing SKILL.md quality, or running automated skill PR reviews.
Produce a comprehensive prompt that hands off the current session's work to a fresh session for sub-agent-orchestrated execution. Use when the user wants to execute discussed/planned work in a new session, run a job to completion via sub-agents, or generate a portable handoff prompt with per-task verification. Assumes the target session supports sub-agents. Triggers on "subagent-prompt", "give me a prompt to run this in a new session", "hand this off to sub-agents", "execute this with sub-agents".
Create Agent Skills with best practices, structure, validation, and testing. Use when designing or refining skills, prompts, references, or supporting files.
Comprehensive Elixir/Phoenix code review with optional parallel agents
Comprehensive iOS/SwiftUI code review with optional parallel agents
Comprehensive Python/FastAPI backend code review with optional parallel agents
Remix v2 meta/SEO, sessions, auth, and CSRF. Use when working with document head, cookie sessions, auth gates, or CSRF protection. Triggers on meta export (v2 array shape), links export, createCookieSessionStorage, commitSession, destroySession, requireUserId, remix-utils/csrf, remix-auth.
Comprehensive React/TypeScript frontend code review with per-area review skills, run in parallel where the agent supports subagents and sequentially otherwise.
Comprehensive Remix v2 code review with per-area review skills, run in parallel where the agent supports subagents and sequentially otherwise. Detects Remix v2 in package.json, loads relevant review skills, runs verification protocol.
Use when the user says "/plan-execute", "plan execute", "implement plan", or "execute plan" and provides a finalized plan file path to carry out. Claude orchestrates, Codex writes code, Claude reviews, and Codex fixes issues until the quality bar is met.
Skillsmith is the canonical lifecycle manager for agent skills (SKILL.md format) across any MCP-capable agent runtime — Claude Code, Cursor, Copilot, Codex, Windsurf. Discover, evaluate, install, use, maintain, author, govern, retire skills. Triggers: 'use Skillsmith', 'ask Skillsmith', 'search Skillsmith', 'find a Skillsmith skill', 'install with Skillsmith', 'Skillsmith trust tier', 'Skillsmith audit', 'create a skill with Skillsmith', 'publish to Skillsmith', 'Skillsmith quota', 'pin a Skillsmith skill', 'compare Skillsmith skills'. Routes natural-language requests to Skillsmith MCP tools and CLI commands.
Discover, install, compare, and manage Claude Code skills. Search the registry, get recommendations, validate skill quality, and manage your installed skills.
Delegate your agent-skill lifecycle: keep skills current, audit and clean up your inventory, and vet skills before you install them. The Skillsmith Agent diagnoses in full for free, proposes a batched fix plan, and changes files only with your per-changeset approval, with one-step undo. Triggers: \"ask the Skillsmith Agent\", \"clean up my skills\", \"what skills are outdated\", \"audit my skills\", \"vet this skill before I install it\".
| Persistent terminal session management for security testing. Use this skill when you need an interactive shell session that maintains state between commands (working directory, environment variables, etc.) during authorized penetration testing.
Connect to an Acquia Source ("Source powered by Drupal CMS") site — its JSON:API and MCP endpoints, OAuth2 auth, the @drupal-canvas CLI, and the Canvas Page API deploy workflow. Use when wiring an AI agent or app to a Source CMS site, building/uploading Drupal Canvas Code Components, deploying landing pages via the Page API, or registering the Source MCP server with Claude Code/Cursor.
Create and manage Claude Code skills following Anthropic best practices and the agentskills.io specification. Use when creating new skills, understanding trigger patterns, debugging skill activation, or implementing progressive disclosure. Covers SKILL.md structure, YAML frontmatter schema, trigger types, enforcement levels, hook mechanisms, and progressive disclosure.
Guides creation and improvement of best-practice agent skills following the open format specification. Covers frontmatter, progressive disclosure, reference files, rules folders, degrees of freedom, constraint calibration, executable scripts, MCP tool references, evaluations, constraint ablation, and an eleven-dimension audit protocol for existing skills. Use when creating a new skill, authoring SKILL.md, setting up a rules-based audit skill, structuring a skill bundle, writing scripts inside a skill, evaluating a skill, improving or rewriting an existing skill, or asking "how to write a skill", "improve this skill", "audit my skill", "review this SKILL.md", "simplify this skill", "is this skill over-constrained", or "rightsize this skill".
>- Audits, scores, and refactors AGENTS.md and CLAUDE.md agent instruction gotchas, signal-to-noise, and @import progressive disclosure. Runs a 12-check quick triage or a 49-check full audit with letter grades, then proposes minimal diffs. Use when asked to audit, review, score, refactor, or improve agent instruction files, fix stale commands, reduce bloat, write a new AGENTS.md, or when asking "my AGENTS.md is bad", "help me write a CLAUDE.md", or "improve my agent instructions". For SKILL.md skill files use agent-skills-creator; for general docs use docs-writing; for mining session history into instruction suggestions use the external cadence-advise skill where installed.
When the user wants to build GTM automation with code, design workflow architectures, use AI agents for GTM tasks, or implement the 'architecture over tools' principle. Also use when the user mentions 'GTM engineering,' 'GTM automation,' 'n8n,' 'Make,' 'Zapier,' 'workflow automation,' 'Clay API,' 'instruction stacks,' 'AI agents for GTM,' or 'revenue automation.' This skill covers technical GTM infrastructure from workflow design through agent orchestration.
When the user wants to choose between PLG and sales-led, design a sales motion, optimize time-to-first-value, or build a value-before-purchase experience. Also use when the user mentions 'PLG,' 'product-led growth,' 'sales-led,' 'sales motion,' 'free trial,' 'freemium,' 'self-serve,' 'demo-first,' 'time-to-first-value,' 'TTFV,' or 'agent-led sales.' This skill covers sales motion selection, value delivery design, and go-to-market motion architecture.
When the user is a solo founder building their GTM motion, wants to scale without hiring, or needs to design an AI agent team for go-to-market. Also use when the user mentions 'solo founder,' 'one-person startup,' 'solopreneur,' 'bootstrapped,' 'no team,' 'AI agents as team,' 'scaling without hiring,' 'founder-led sales,' 'lean GTM,' 'one-person company,' or 'no employees.' This skill covers the complete solo founder GTM playbook from stack selection through agent team design, revenue-stage transitions, time allocation, and when to finally hire.