agentleFS
hardness1020hardness1020/VibeFlow/.claude/skills/clarify-demand/SKILL.md

clarify-demand

Skill8 starsChanged 8 months agoMITView on GitHub

Conversational demand clarification and feasibility research before formal work item registration

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Ask the user to clarify the following (skip items they've already provided):

    line 29
  2. Distinctive:

    Keep the conversation natural.

    line 37
  3. Distinctive:

    If they want changes, loop back to the relevant phase.

    line 75
  4. Distinctive:

    Bias toward smaller tracks: When in doubt, recommend Small over Medium.

    line 80

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

clarify-demand

Conversational demand clarification and feasibility research before formal work item registration.

Purpose

This skill helps users go from a vague idea to a concrete work item registration. It runs before /manage-work register to ensure the right track, scope, and description are chosen.

Works on any branch (including main).

4-Phase Workflow

Phase 1: Demand Clarification

Ask the user to clarify the following (skip items they've already provided):

  1. Problem — What problem are you solving? What's broken or missing?
  2. Users — Who benefits from this? (end users, developers, ops, etc.)
  3. Done Criteria — How will you know it's done? What does success look like?
  4. Constraints — Any technical, time, or compatibility constraints?
  5. Scope Signal — Is this a bug fix, a single feature, a multi-component change, or a system-level change?

Keep the conversation natural. Ask follow-up questions if answers are vague. One or two rounds is usually enough.

Phase 2: Feasibility Research

Perform read-only codebase analysis:

  • Does existing code support this, or is new infrastructure needed?
  • What files/modules would be affected?
  • Are there existing patterns to follow?
  • What are the risks (high coupling, missing tests, architectural debt)?

Skip this phase for obvious Micro/Small items where the scope is clear and the change is localized.

Phase 3: Synthesis

Produce an Intake Brief (displayed in conversation, not persisted to disk):

## Intake Brief

**Problem**: [1-2 sentences]
**Users**: [who benefits]
**Feasibility**: [summary from Phase 2, or "Skipped — scope is clear"]
**Complexity**: [Low / Medium / High]
**Recommended Track**: [Micro / Small / Medium / Large]
**Rationale**: [why this track]
**Suggested Description**: "<description for register command>"
**Suggested ID**: <numeric ID>

Phase 4: Handoff

Present the Intake Brief to the user. If they confirm:

/manage-work register "<description>" <ID> <track>

If they want changes, loop back to the relevant phase.

Guidelines

  • Ephemeral output: The Intake Brief lives in the conversation only — no files are written.
  • Bias toward smaller tracks: When in doubt, recommend Small over Medium. Users can always upgrade.
  • Don't over-research: Phase 2 should take 1-2 minutes, not 10. Quick scan, not deep dive.
  • Natural language: This is a conversation, not a form. Adapt to the user's communication style.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.