speckit.clarify
The `speckit.clarify` command identifies ambiguous or underspecified areas in a feature specification by performing a structured coverage scan across functional scope, data model, UX flows, non-functional attributes, integrations, and edge cases. It then poses up to five targeted clarification questions and encodes the answers directly into the spec file. Use this command before invoking `/speckit.plan` to reduce downstream rework risk and ensure critical decisions are documented.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/maslennikov-ig/claude-code-orchestrator-kit/HEAD/.claude/commands/speckit.clarify.md -o ~/.claude/commands/speckit.clarify.mdspeckit.clarify.md
## User Input
```text
$ARGUMENTS
```
You **MUST** consider the user input before proceeding (if not empty).
## Outline
Goal: Detect and reduce ambiguity or missing decision points in the active feature specification and record the clarifications directly in the spec file.
Note: This clarification workflow is expected to run (and be completed) BEFORE invoking `/speckit.plan`. If the user explicitly states they are skipping clarification (e.g., exploratory spike), you may proceed, but must warn that downstream rework risk increases.
Execution steps:
1. Run `.specify/scripts/bash/check-prerequisites.sh --json --paths-only` from repo root **once** (combined `--json --paths-only` mode / `-Json -PathsOnly`). Parse minimal JSON payload fields:
- `FEATURE_DIR`
- `FEATURE_SPEC`
- (Optionally capture `IMPL_PLAN`, `TASKS` for future chained flows.)
- If JSON parsing fails, abort and instruct user to re-run `/speckit.specify` or verify feature branch environment.
- For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'\''m Groot' (or double-quote if possible: "I'm Groot").
2. Load the current spec file. Perform a structured ambiguity & coverage scan using this taxonomy. For each category, mark status: Clear / Partial / Missing. Produce an internal coverage map used for prioritization (do not output raw map unless no questions will be asked).
Functional Scope & Behavior:
- Core user goals & success criteria
- Explicit out-of-scope declarations
- User roles / personas differentiation
Domain & Data Model:
- Entities, attributes, relationships
- Identity & uniqueness rules
- Lifecycle/state transitions
- Data volume / scale assumptions
Interaction & UX Flow:
- Critical user journeys / sequences
- Error/empty/loading states
- Accessibility or localization notes
Non-Functional Quality Attributes:
- Performance (latency, throughput targets)
- Scalability (horizontal/vertical, limits)
- Reliability & availability (uptime, recovery expectations)
- Observability (logging, metrics, tracing signals)
- Security & privacy (authN/Z, data protection, threat assumptions)
- Compliance / regulatory constraints (if any)
Integration & External Dependencies:
- External services/APIs and failure modes
- Data import/export formats
- Protocol/versioning assumptions
Edge Cases & Failure Handling:
- Negative scenarios
- Rate limiting / throttling
- Conflict resolution (e.g., concurrent edits)
Constraints & Tradeoffs:
- Technical constraints (language, storage, hosting)
- Explicit tradeoffs or rejected alternatives
Terminology & Consistency:
- Canonical glossary terms
- Avoided synonyms / deprecated terms
Completion Signals:
- Acceptance criteria testability
- Measurable Definition of Done style indicators
Misc / Placeholders:
- TODO markers / unresolved decisions
- Ambiguous adjectives ("robust", "intuitive") lacking quantification
For each category with Partial or Missing status, add a candidate question opportunity unless:
- Clarification would not materially change implementation or validation strategy
- Information is better deferred to planning phase (note internally)
3. Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:
- Maximum of 10 total questions across the whole session.
- Each question must be answerable with EITHER:
- A short multiple‑choice selection (2–5 distinct, mutually exclusive options), OR
- A one-word / short‑phrase answer (explicitly constrain: "Answer in <=5 words").
- Only include questions whose answers materially impact architecture, data modeling, task decomposition, test design, UX behavior, operational readiness, or compliance validation.
- Ensure category coverage balance: attempt to cover the highest impact unresolved categories first; avoid asking two low-impact questions when a single high-impact area (e.g., security posture) is unresolved.
- Exclude questions already answered, trivial stylistic preferences, or plan-level execution details (unless blocking correctness).
- Favor clarifications that reduce downstream rework risk or prevent misaligned acceptance tests.
- If more than 5 categories remain unresolved, select the top 5 by (Impact * Uncertainty) heuristic.
4. Sequential questioning loop (interactive):
- Present EXACTLY ONE question at a time.
- For multiple‑choice questions:
- **Analyze all options** and determine the **most suitable option** based on:
- Best practices for the project type
- Common patterns in similar implementations
- Risk reduction (security, performance, maintainability)
- Alignment with any explicit project goals or constraints visible in the spec
- Present your **recommended option prominently** at the top with clear reasoning (1-2 sentences explaining why this is the best choice).
- Format as: `**Recommended:** Option [X] - <reasoning>`
- Then render all options as a Markdown table:
| Option | Description |
|--------|-------------|
| A | <Option A description> |
| B | <Option B description> |
| C | <Option C description> (add D/E as needed up to 5) |
| Short | Provide a different short answer (<=5 words) (Include only if free-form alternative is appropriate) |
- After the table, add: `You can reply with the option letter (e.g., "A"), accept the recommendation by saying "yes" or "recommended", or provide your own short answer.`
- For short‑answer style (no meaningful discrete options):
- Provide your **suggested answer** based on best practices and context.
- Format as: `**Suggested:** <your proposed answer> - <brief reasoning>`
- Then output: `Format: Short answer (<=5 words). You can accept theCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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