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
> Use when the user wants to apply a named Higgsfield motion preset, asks about VFX presets, transformation effects, elemental effects, or transition presets. Contains the full named preset library with descriptions and prompt usage.
Turns a brief, script, scene breakdown, treatment, or story idea into ONE connected director's shotlist for Seedance 2.0 — a single editable HTML artifact with a global Style Prefix, an @-asset glossary, and named per-scene prompts (1a, 1b, 2a…) each in Style → Characters → Scene → CUT 1..N form. Use whenever the user says 'make a shotlist', 'break this script into prompts', 'generate Seedance prompts for this ad/film', 'turn this brief into a shot list', 'director's shotlist', or wants many connected scene prompts rather than one. Also use to revise an existing shotlist (edit-once-propagates: 'change the style prefix everywhere', 'rewrite scene 4', 'split prompt 6'). Each prompt targets 15s; longer scenes split across 1a/1b/1c under one scene number.
> Use when a Higgsfield generation fails, produces poor quality, looks wrong, doesn't match the prompt, or the user needs to fix or improve an output.
Advanced AI agent benchmark scenarios that push Vercel's cutting-edge platform features — Workflow DevKit, AI Gateway, MCP, Chat SDK, Queues, Flags, Sandbox, and multi-agent orchestration. Designed to stress-test skill injection for complex, multi-system builds.
Audit vercel-plugin performance on real-world projects. Extracts tool calls from Claude Code conversation logs, tests hook matching against actual inputs, identifies pattern coverage gaps, and checks plugin cache staleness. Use when asked to audit, test, or investigate plugin skill injection on a real project.
Run live eval sessions against the vercel-plugin to verify hook behavior, skill injection, dedup correctness, and coverage. Launches real Claude Code sessions via WezTerm, monitors debug logs, and produces a structured coverage report.
Run vercel-plugin eval scenarios in Vercel Sandboxes instead of local WezTerm panels. Provisions ephemeral microVMs with Claude Code + plugin pre-installed, runs benchmark prompts, extracts hook artifacts, and produces coverage reports.
Build durable backend AI agents with the eve framework. Use when creating, editing, or debugging an eve project — agent instructions, skills, tools, connections, channels, sandboxes, subagents, schedules, or evals.
Vercel Connect expert guidance — securely obtain scoped OAuth tokens for third-party services (Slack, GitHub, MCP servers, OAuth, Snowflake) on behalf of apps or users via Vercel OIDC. Use when wiring up third-party API access, connecting to MCP servers, sending Slack messages, accessing GitHub APIs, receiving webhook events from Slack/Linear/GitHub and forwarding them to your agents and apps, or building Eve agent connections.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Sync skills between local installation and the GitHub source-of-truth repository. Use when asked to install, update, list, or push skills.
Automatically update OpenClaw and selected skills once daily. Runs via cron, checks for updates, applies them, and messages the user with a summary of what changed.
This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices.
Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.
Coordinate plan files by launching multiple parallel subagents for unblocked tasks. Triggers on explicit "/parallel-task" commands.
Create structured plans for multi-task projects that can be used by the task-orchestrator skill. Use when breaking down complex work into parallel and sequential tasks with dependencies.
Research any topic from the last 30 days on Reddit + X + Web, synthesize findings, and write copy-paste-ready prompts. Use when the user wants recent social/web research on a topic, asks "what are people saying about X", or wants to learn current best practices. Requires OPENAI_API_KEY and/or XAI_API_KEY for full Reddit+X access, falls back to web search.
> This skill should be used when the user asks to "analyze a competitor", "compare pricing", "competitive landscape", "market research", "what do customers think", "review intelligence", "hiring signals", "content strategy", "SEO battle", "build a battlecard", "competitive analysis", "who are the players", "who competes with", "market intelligence", "competitive positioning", "deep dive on a company", "board prep", "SWOT analysis", "how does [X] compare to [Y]", or mentions competitor analysis, pricing comparison, customer sentiment, or market landscape research. Requires Apify CLI or Apify MCP server.
The operating doctrine, self-contained in this folder. Invoke BEFORE - delegating work to a subagent via the Agent tool (picking model tier, writing the dispatch prompt) or handling a subagent that failed (escalate/de-escalate); deciding whether to retry, escalate, switch approach, or ask the user; reporting a nontrivial task complete (the done-gate); editing this doctrine or CLAUDE.md; recording a lesson about the harness.
The engineering loop - select, advance, distill, deliver - for running a work session on a project. Invoke when STARTING a session or picking the next thing to work on; sizing a unit of work or matching it to your model tier; running long/autonomously and deciding pace or when to stop; deciding where a learning should live (lesson vs memory vs project-level skill); or wrapping up a unit/session (commit, report, handoff note).
AI agent and LLM system engineering reference covering single-agent dev (ReAct, tool calling, plan-execute), multi-agent coordination (swarm, role decomposition, file locking), LLM security (prompt injection, jailbreak defense, output filtering), RAG architecture (chunking, hybrid retrieval, rerank), and prompt engineering / evaluation (RAGAS, LLM-as-Judge). Use when building AI agents, designing RAG pipelines, orchestrating multi-agent workflows, hardening LLM apps, or writing prompts.
AI-automated hardware product pipeline: from requirements to manufacturable Gerber + compiled firmware + cross-platform app. Orchestrates system design, ESP-IDF firmware, KiCad PCB, UniApp client, and release packaging in a zero-human-touch flow.
Distills repeated workflows into reusable skills, improves existing skills, and gates them through a three-tier publish funnel (local → project → community). Use when the agent or user notices a pattern worth crystallizing, when an existing skill is incomplete, or when something deserves community contribution. Default deny on dangerous tools and side effects.
KiCad EDA orchestration via kicad-mcp MCP server. Routes 17 tools for schematic creation, PCB layout, autorouting, DRC, and Gerber export. Enforces serialized PCB ops, library-first lookup, and autoroute-only routing.
Multi-agent adversarial-verification orchestration for high-confidence conclusions. Fan-out finders, then verify every finding through a three-prism panel (exploitability / correctness / refutation) that defaults to disbelief, gate fixes behind load-bearing proof tests that catch agents who falsely claim "done/fixed", and roll out behind a build-first exit-code guard. Use when a fan-out task must produce trustworthy results — security audit, code review, research synthesis, migration — and a single agent's self-report cannot be trusted. Composes with securing-systems (what to look for) and shipping-changes (change closed loop); orchestration engine is the Workflow tool.
Generate tests by exploring your app with the AI vision agent
Use AI coding agents and exploration mode to generate TestDriver tests
Design lens critique covering visual hierarchy, clarity, and anti-slop patterns — produces a findings table, no code edits unless asked. Use when the user wants a design review, says "what's wrong with this UI", or needs a second opinion before a handoff or presentation. Invoke when the user asks for critique on their UI, or mentions 'critique' alongside design / UI / frontend work.
Create Agent Bricks: Knowledge Assistants (KA) for document Q&A and Supervisor Agents for multi-agent orchestration (MAS).
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.
Uses Agent SDK to deploy 3 parallel agents for client onboarding -- workflow auditor, tech stack mapper, and strategy drafter. Real consulting workflow that produces a complete client assessment.
Design sport-specific practice sessions. Drills by skill level and time, warm-up, skill work, scrimmage, cool-down.
Enhanced skill navigator that maps conversation history, recommends multi-skill chains, identifies patterns from past usage, and learns from session outcomes. Goes beyond basic scout with deep context analysis and workflow orchestration.
Analyzes current conversation context to recommend the best skills and subagents for the task at hand. Use proactively when unsure which tool, skill, or agent to use.
CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.
The 100th skill! Your intelligent guide to all 99 other skills. Recommends the perfect skill for any task, creates skill combinations, and helps you discover capabilities you didn't know you had.
Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.
Create GRACE 4 worker and reviewer subagent presets that understand .grace artifacts, scopes, assertions, and verification evidence.
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
仅当用户显式调用 `$handoff`，或明确要求生成会话交接文档时使用。将当前对话压缩为脱敏、可执行的交接文档，供下一个 agent 接续工作；不要自动触发。
> Convert a personal agent skill into a distributable, open-source-ready skill. Runs PII audit, generalization, license compatibility check, cross-platform adapter review, and packaging workflow.
Diagnostic checklist for the MedSci Skills runtime. Verifies Python, R, Node, Claude Code, Git, Zotero, and configured MCP servers, and prints a pass/fail table with links to the right setup doc for any missing component. Read-only — does not install anything.
Use when work should be delegated to Claude Code CLI, especially headless `claude -p` runs, automation scripts, CI jobs, resumable sessions, or requests to use Claude/Claude Code for a task.
> Evaluate skill quality, find the weakest dimension, and apply directed improvements. Also tracks usage to spot idle or risky skills. evaluation, inbox, suggestions, or improvement.