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skill-studio

Design Community
Fluxly profile Facts only: domain, agents, trust score, runtime, permissions and network
Domain
Design
Compatible agents
  • Claude Code
  • Cursor
  • Cline
  • Codex
  • Windsurf
  • Gemini CLI
  • +20
Trust score
88 / 100 · community maintained
Author / version / license
@glebis · no license declared
Token usage
Moderate
Setup complexity
Guided setup
External API key
Required · Anthropic
Operating systems
Unspecified (assume cross-platform)
Runtime requirements
Python >=3.11
Permissions
  • Read-only
  • Write / modify
Network behavior
External requests
Install commands
26 variants

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview skill-studio.preview 
---
name: skill-studio
description: Interview-driven automation design tool. This skill should be used when the user wants to design…
category: design
runtime: Python
---

# skill-studio output preview

## PART A: Task fit
- Use case: Interview-driven automation design tool. This skill should be used when the user wants to design a new skill, agent, automation, shortcut, or any other automatable workflow. Runs a coverage-driven JTBD interview (text or voice), then exports a one-page markdown spec plus an SVG design map. Can also analyze Claude Code sessions (current or by ID) to extract workflows, subagent patterns, and skill usage, then propose new skills based on observed patterns. Use this skill whenever the user mentions analyzing a session, extracting workflows, proposing skills from past work, reviewing what tools or agents were used, or turning a session into a skill. Triggers on "analyze this session", "what skills could I build from this", "propose skills from session", "what workflows did I use", "what did I do in this session", "extract patterns", "turn this into a skill"..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Purpose / Architecture / When to use” and do not present inference as author intent.

## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Interview-driven automation design tool. This skill should be used when the user wants to design a new skill, agent, automation, shortcut, or any other automatable workflow. Runs a coverage-driven JTBD interview (text or voice), then exports a one-page markdown spec plus an SVG design map. Can also analyze Claude Code sessions (current or by ID) to extract workflows, subagent patterns, and skill usage, then propose new skills based on observed patterns. Use this skill whenever the user mentions analyzing a session, extracting workflows, proposing skills from past work, reviewing what tools or agents were used, or turning a session into a skill. Triggers on "analyze this session", "what skills could I build from this", "propose skills from session", "what workflows did I use", "what did I do in this session", "extract patterns", "turn this into a skill".”.
- **02** When the source has headings, the agent prioritizes “Purpose / Architecture / When to use” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; requires Anthropic API keys.

## Running Rules
- read files, write/modify files; may access external network resources; requires Anthropic API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.
Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

  • Core job: Interview-driven automation design tool. This skill should be used when the user wants to design a new skill, agent, automation…
  • Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
  • Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

  • Structure: The skill is organized around “Purpose”, “Architecture”, “When to use”, “Prerequisites”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
  • Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
  • Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

  • Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
  • Invocation: Name skill-studio directly; if the source includes slash commands, start with the command and then add task context.
  • Validation: Start small and check whether the result follows “Purpose / Architecture / When to use” before expanding.

Boundaries And Review

  • Dependencies: Prepare Anthropic API keys before running a full task.
  • Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
  • Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Discussion

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