atelier-orchestrator
Skill routing and workflow orchestration. Selects Inline Plan or Spec-backed Plan, routes to the correct workflow skill, and manages transitions between…
Conversational design workshop for substantial work. Interviews the human one question at a time, explores 2-3 approaches with trade-offs, and presents the design section by section for approval before writing only design.md, then stops. Combines requirements discovery with
$ npx -y skills add martinffx/atelier --skill spec-brainstorm --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/spec-brainstormContext preview
The summary Claude sees to decide when to auto-load this skill.
Conversational design workshop for substantial work. Interviews the human one question at a time, explores 2-3 approaches with trade-offs, and presents the design section by section for approval before writing only design.md, then stops. Combines requirements discovery with
name: spec-brainstorm description: > Conversational design workshop for substantial work. Interviews the human one question at a time, explores 2-3 approaches with trade-offs, and presents the design section by section for approval before writing only design.md, then stops. Combines requirements discovery with codebase research and architecture design. Use when the user explicitly requests a spec or when atelier-orchestrator selects a Spec-backed Plan. Ambiguous design or discovery requests route through atelier-orchestrator. user-invocable: true argument-hint: <topic or feature description>
Conversational design workshop for substantial work that produces a focused, reviewed spec.
One question at a time. Multiple approaches explored. Design approved in sections. Ruthless scope control. No implementation until design is approved.
Run this skill only after `atelier-orchestrator` selects a Spec-backed Plan or the human explicitly requests a spec. Bounded work should go directly to `spec-plan` for an Inline Plan. Do not reclassify the planning mode here.
docs/specs/YYYY-MM-DD-<feature>/ └── design.md ← This skill's output
Requirements are inline — no separate requirements.json needed.
This skill may create or update only the `design.md` shown above. It may inspect the repository and discuss drafts in conversation, but it must not modify any other file, create `plan.json`, create tracker entries, invoke another workflow skill, or write implementation code. This boundary still applies when the human asks to brainstorm, plan, and implement in one request. Finish `design.md`, report it, and stop.
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These principles apply to every spec, every time.
Persisted specs are for work whose discovery, architecture, dependencies, or coordination needs justify a durable artifact. Do not pull bounded work into this workflow merely because it touches multiple files or takes time. That work belongs in an Inline Plan.
Break the system into units with one clear purpose each. Well-defined interfaces between them. Each unit independently understandable and independently testable. If you can't explain a unit's job in one sentence, it's doing too much.
Explore the current structure first. Follow existing patterns. Targeted improvements only. No unrelated refactoring. Understand why things are the way they are before proposing changes. Treat loaded skills as relevant guidance, not as a requirement to apply every pattern they contain.
If the request describes multiple independent subsystems, flag it immediately. Decompose into sub-projects before diving into details. Each substantial sub-project gets its own spec and plan; bounded sub-projects can use Inline Plans. A spec that tries to cover three subsystems helps no one.
Keep migrations separate from authorization, product behavior, infrastructure, schema, and test-platform projects. Do not use a migration as permission to redesign adjacent systems.
Remove unnecessary features from all designs. If a capability isn't needed for the first user story, it doesn't go in the spec. Every feature is a cost — to build, to test, to maintain, to understand later. Push back on scope creep during discovery.
Future consumers do not justify shared infrastructure. A "reusable foundation" may describe an architectural quality, but it is not a user story or a current requirement.
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Before diving in, understand where you are.
1. **Read project context** — AGENTS.md, README, existing architecture docs 2. **Check existing specs** — Scan `docs/specs/` for previous work. What domain model exists? What patterns are established? What has been built before? 3. **Read recent specs** — What was the last thing built? Is this feature building on existing work, extending it, or something greenfield?
This is silent — don't narrate it. Let the context inform where you focus.
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Ask questions to understand what to build. Skip this step if requirements are already clear from context (existing specs, human provided details, etc.).
Ask **one question at a time**. Multiple choice preferred when possible — give 2-4 concrete options rather than open-ended prompts. Keep the conversation moving.
**Good:** "Should this be real-time or batch-processed? (a) Real-time via WebSocket, (b) Periodic polling every 30s, (c) On-demand when user requests it."
**Bad:** "How should the data synchronization work?"
Before asking any detail questions, assess scope. If the request describes multiple independent subsystems (e.g., "build a notification system with email, SMS, push, and an admin dashboard"):
1. **Flag it immediately:** "This looks like multiple independent projects. Let me propose a decomposition." 2. **Break it down:** Identify the subsystems and their dependencies. 3. **Get agreement:** "Which of these should we spec first?"
Do not try to spec everything in one document.
During discovery, push back on scope:
If the human insists, include it — but flag the trade-off in the spec.
Adapt these to context. Not all are needed every time.
1. **What problem are we solving?** — Concrete problem statement, not solution description 2. **Who has this problem?** — User roles 3. **How do they solve it today?** — Current
A personal development toolkit for AI agents. It covers spec-driven development, code quality, and deep thinking. Atelier gives coding agents a disciplined way to move from an idea to reviewed, verified code without taking control away from the developer.
Repo: martinffx/atelier
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