mcpbeat Sign in

Workflow Help Skill for Claude

Interactive guide to workflow system: agents, skills, routing, execution patterns.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
413
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/notque/vexjoy-agent --skill workflow-help

The instruction itself

12 sections, as written by the author

Workflow Help Skill

Overview

This skill operates as an educational guide for repository workflows. It answers questions about how the agent/skill/routing architecture works, what tools and components are available, and when to use each workflow phase (brainstorm, plan, execute). The skill prioritizes accuracy over speed by reading actual SKILL.md and agent files rather than relying on memory.


Instructions

Phase 1: UNDERSTAND THE QUESTION

Goal: Determine exactly what the user wants to know about.

Parse the user's topic and $ARGUMENTS. Common categories:

  • brainstorm / plan / execute - Workflow phases
  • skills / agents / hooks - Component types
  • routing / do - How routing works
  • subagent - Subagent-driven execution
  • No argument - Provide system overview

Constraint (Over-Engineering Prevention): Answer only what was asked. Do not dump the entire system architecture when the user asks about one skill. Scope your response to the question asked, then offer to explain related concepts.

Gate: Topic identified. Proceed only when you know what to explain.

Phase 2: GATHER ACCURATE INFORMATION

Goal: Read actual files before explaining anything.

Step 1: Get authoritative data from the catalog script

This constraint (Accuracy Over Speed) is non-negotiable. Counts and listings come from scripts/list-capabilities.py, which reads the generated INDEX files — deterministic, single source of truth. Match the question to the right subcommand:

| Question | Command | Output |

|----------|---------|--------|

| Overview / "how many skills/agents?" | python3 scripts/list-capabilities.py summary | Skills / Pipelines / Agents counts |

| "what skills exist [in category X]?" | python3 scripts/list-capabilities.py skills [--category X] --brief | Count; drop --brief for the full name/trigger/description table |

| "what agents exist?" | python3 scripts/list-capabilities.py agents --brief | Count; drop --brief for the full table |

| "tell me about <name>" | python3 scripts/list-capabilities.py show <name> | Type, description, triggers, category, file path for a skill/agent/pipeline |

| Fuzzy lookup / "is there a skill for X?" | python3 scripts/list-capabilities.py search <query> | Ranked name/trigger/description matches across skills, pipelines, agents |

--category X filters skills by keyword in name or description (e.g. voice, game, kubernetes); agents filter by exact category field. show <name> exits 1 when the name is absent — fall back to search <name> to suggest the closest match.

Step 2: Read the actual file for deep questions

The script gives authoritative counts, names, and one-line descriptions. For anything deeper — phases, gates, capabilities, when-to-use — read the file the script names in its File: field:

  • For a specific skill: Read skills/{path-from-show}/SKILL.md
  • For a specific agent: Read agents/{agent-name}.md
  • For routing overview: Check the /do router configuration

Extract: name, description, version, what it CAN and CANNOT do, how to invoke it, related skills or agents.

Staleness: summary, skills, and agents exit 2 and print a stderr warning when source files are newer than the INDEX. If you see that warning, tell the user to regenerate: python3 scripts/generate-skill-index.py (skills) or python3 scripts/generate-agent-index.py (agents), then re-run.

Constraint (No Fabrication): If a skill or agent does not exist, say so rather than inventing capabilities. If a skill or agent was recently deleted or merged, search with Glob for similar names and suggest the closest match.

Gate: Information gathered from actual files, not memory. Proceed only when gate passes.

Phase 3: EXPLAIN CLEARLY

Goal: Present information in the format most useful for the user's question.

For system overview, lead with live counts from python3 scripts/list-capabilities.py summary, then present the execution architecture:

Router (/do) -> Agent (domain expert) -> Skill (methodology) -> Script (execution)

Then show key workflow:

  • BRAINSTORM - Clarify requirements, explore approaches
  • WRITE-PLAN - Break into atomic, verifiable tasks
  • EXECUTE - Direct or subagent-driven execution
  • VERIFY - Run tests, validate changes

For specific components, use this format:

## [Component Name]
**Type**: Skill / Agent / Hook
**Invoke**: /command or skill: name
**Purpose**: One-sentence description
**Key Phases/Capabilities**: Bulleted list
**Related**: Links to related components

For "when to use what", use a decision table:

| You Want To... | Use This |

|----------------|----------|

| Start a new feature | /do implement [feature] |

| Debug a bug | /do debug [issue] |

| Review code | /do review [code] |

| Execute an existing plan | skill: subagent-driven-development |

| Create a PR | /pr-workflow |

Constraint (Show Real Examples): Reference actual skill names, commands, and file paths from this repository. Use tables for lists when presenting available skills, agents, and commands. Include invocation syntax for each component mentioned. Apply progressive disclosure: start with overview, offer deeper detail on request. Cross-reference related skills and agents when explaining one component.

Step: Offer next steps

After explaining, ask if the user wants to:

  • Learn about a related component
  • Actually execute a workflow (route to appropriate skill)
  • See more detail on a specific aspect

Constraint (Route When Appropriate): If user actually wants to execute a workflow, detect the execution intent and route to the correct skill instead of explaining it. For example, if user asks "how do I debug X" meaning "debug X for me", recognize the intent is execution and route to systematic-debugging, not an explanation of the debugging process.

Gate: User's question answered with information from actual files.


Error Handling

Error: "Skill or Agent Not Found"

Cause: User asked about a component that does not exist or was renamed

Solution:

  • Search with Glob for similar names
  • Check if it was recently deleted or merged
  • Suggest the closest matching component

Error: "User Wants Execution, Not Explanation"

Cause: User asked "how do I debug X" meaning "debug X for me"

Solution:

  • Recognize the intent is execution, not education
  • Route to the appropriate skill (e.g., systematic-debugging)
  • Do not explain the debugging process; invoke it

Error: "Stale INDEX"

Cause: scripts/list-capabilities.py exited 2 with a stderr warning — source files are newer than the generated INDEX

Solution:

  • Tell the user to regenerate: python3 scripts/generate-skill-index.py and/or python3 scripts/generate-agent-index.py
  • Re-run the catalog command; the counts now reflect current files
  • For deep content, read the file fresh rather than relying on cached descriptions

References

Core Constraints Embedded in Workflow

This skill is built on five hardcoded constraints that must always apply:

  • CLAUDE.md Compliance: Read and follow repository CLAUDE.md before answering any question
  • Accuracy Over Speed: Get counts and listings from scripts/list-capabilities.py (deterministic, INDEX-backed); read the actual SKILL.md and agent file for any deeper detail. Describe components from these sources, not from memory.
  • Show Real Examples: Reference actual skill names, commands, and file paths from this repository
  • No Fabrication: If a skill or agent does not exist, say so rather than inventing capabilities
  • Route When Appropriate: If user actually wants to execute a workflow, route to the correct skill instead of explaining it

The skill's default behaviors reinforce accuracy:

  • Scope to the specific question asked (over-engineering prevention)
  • Use tables for presenting lists of skills, agents, and commands
  • Include invocation syntax for every component mentioned
  • Apply progressive disclosure: start with overview, deepen on request
  • Cross-reference related components when explaining one

Optional advanced modes (disabled by default):

  • Full Architecture Dump: Explain the entire Router → Agent → Skill → Script pipeline
  • Comparison Mode: Compare two skills or agents side-by-side
  • Troubleshooting Guide: Help diagnose why a skill or route isn't working as expected

Other skills for the same job

different authors, same section of the catalogue
Declarative Agents
by github
vendor ×1

Complete development kit for Microsoft 365 Copilot declarative agents with three comprehensive workflows (basic, advanced, validation), TypeSpec support, and Microsoft 365 Agents Toolkit integration

1k tokens
Treatment Plans
by K-Dense-AI
×1

Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

38k tokens scripts
Okx AI
by internet-court
×1

> provider/change budget/修改卖家/修改预算/draft/草稿/我的任务/my tasks/what am I working on/关闭/取消任务/决策列表/decision list/指定服务商/browse (sender.role = COUNTERPARTY, not you); (3) literal "Read the okx-ai skill" (or legacy "Read the okx-agent-task skill") in the envelope.

57k tokens
Prior Auth Review Skill
by anthropics
vendor ×1

Automate payer review of prior authorization (PA) requests. This skill should be used when users say "Review this PA request", "Process prior authorization for [procedure]", "Assess medical necessity", "Generate PA decision", or when processing clinical documentation for coverage policy validation and authorization decisions.

23k tokens
AI Agents Architect
by lingxling
×1

Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

2k tokens
Autonomous Agents
by lingxling
×1

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

7k tokens
Design Orchestration
by lingxling
×1

Orchestrates design workflows by routing work through brainstorming, multi-agent review, and execution readiness in the correct order.

959 tokens
Pitchcraft
by moshuying
×1

Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.

5k tokens

How to use it

Copy the folder

Take notque/workflow-help from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.