Clarify

Resolve ambiguity before action. This skill turns vague, incomplete, or contradictory implementation requests into precise, actionable scope through structured clarification or documented assumptions.

Invocation posture: hybrid. Prefer explicit invocation. Automatic activation should stay limited to high-confidence implementation ambiguity, not general idea exploration.

When To Activate

• The user explicitly asks to clarify requirements, disambiguate a request, or spec a change before implementation
• An implementation request contains direct contradictions or mutually exclusive constraints
• Acceptance criteria or scope boundaries are missing in a way that would materially change what gets built
• The user asks you to proceed with assumptions and those assumptions need to be made explicit
• Multiple plausible implementations exist and choosing among them would change files, APIs, or user-visible behavior

When Not To Use

• Open-ended option exploration, strategy work, or direction-finding; use brainstorming
• Requests that mainly ask "what should we do?" rather than "what exactly should we build?"
• Specific actionable tasks with clear files, error messages, or expected outcomes
• Read-only explanation, code reading, or search tasks where no implementation decision is blocked
• Cases where the user explicitly says to proceed without questions and there is no contradiction or material ambiguity to surface

Outcome

Produce the smallest clarification artifact that unblocks execution:

• A concise clarification summary with clarified scope, key decisions, and out-of-scope items
• A short set of hypothesis-driven user questions when genuine T3 judgments remain
• An assumptions log when running non-interactively or when the user wants you to proceed without waiting

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Core Principles

1. Contradictions first, details second. Resolve conflicting goals before discussing implementation detail.
2. Research before asking. Mine the codebase, docs, tests, history, and current conversation first.
3. Hypotheses, not open questions. Offer plausible options with a recommendation instead of asking the user to design the answer from scratch.
4. Minimum viable clarification. Ask only what would materially change the implementation.
5. Document decisions. Every resolved ambiguity should be traceable to evidence, best practice, user choice, or explicit assumption.
6. Stay out of brainstorming territory. If the real issue is choosing a direction rather than clarifying requirements, hand off to brainstorming.
7. Match the user's tempo. If they say "proceed" or "don't ask," switch to autonomous clarification and record assumptions instead of interrogating them.
8. Follow the user's language. User-facing questions, summaries, and assumptions logs should use the user's language.

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Workflow

Phase 0 — Detect and Assess

Capture the request and classify the ambiguity:

• Stated goals: What they explicitly asked for
• Implicit goals: What they probably want but didn't say
• Constraints mentioned: Any boundaries, deadlines, or requirements stated
• Gaps: What's missing that you'd need to know to implement confidently
• Ambiguity types: contradiction, vagueness, incompleteness, scope ambiguity, behavioral ambiguity, terminology, priority ambiguity
• Risk level:
  • LOW: single obvious interpretation; proceed
  • MEDIUM: some ambiguity; ask 1-3 targeted questions if needed
  • HIGH: destructive or high-impact ambiguity; clarify before action
• Confidence check:
  • 90-100%: proceed and note assumptions inline
  • 70-89%: quick clarification
  • below 70%: full clarification flow

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Phase 1 — Research Before Asking

Gather evidence before asking the user anything:

• Read related files, tests, docs, and instruction files
• Check recent history or existing conventions when they can answer scope questions
• Reuse conversation context, mentioned files, and surrounding patterns

Classify each ambiguity into decision tiers:

Tier	Definition	Action
T1 — Evidence exists	Codebase, docs, or conventions answer the question	Resolve autonomously, cite evidence
T2 — Best practice consensus	Clear industry standard or framework convention applies	Resolve autonomously, note the standard
T3 — Judgment call	Conflicting evidence, subjective preference, or novel decision	Must ask the user

Only T3 items should generate questions for the user. T1 and T2 items are resolved autonomously with documented reasoning.

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Phase 2 — Clarify Interactively Or Autonomously

If T3 decisions remain, choose the lightest mode that will safely unblock the task.

Interactive clarification

• Present 2-4 concrete options, not open-ended prompts
• Include a recommendation and short rationale
• Batch only related questions
• Default limits:
  • MEDIUM risk: 1-3 questions
  • HIGH risk: 3-7 questions
• Stop early if the user says to proceed or defer to your judgment

Autonomous clarification

• Resolve T1 with evidence
• Resolve T2 with best-practice defaults
• Resolve T3 with the least surprising, most reversible choice
• Mark human-review assumptions clearly so they can be overridden later

See templates.md for question patterns, example wording, and an assumptions-log template.

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Phase 3 — Deliver And Hand Off

Summarize only what execution needs next:

• Original request
• Clarified scope
• Key decisions with source labels (T1, T2, T3)
• Out of scope
• Remaining uncertainties or assumptions that still need review

Then hand off appropriately:

• proceed to implementation when scope is now clear
• hand off to writing-plans if execution needs a more detailed plan
• hand off to brainstorming if the unresolved issue is choosing a direction
• hand off to deep-research if clarification reveals a research-first problem
• hand off to review if the user wants existing work evaluated against clarified criteria

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For reusable output patterns, context-specific prompts, and cross-skill boundaries, see:

• templates.md
• context-and-handoffs.md

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Validation

Validate the canonical package and its durable fixtures before shipping changes:

• uv run python skills/skill-lifecycle-manager/scripts/quick_validate.py skills/clarify
• uv run python skills/skill-lifecycle-manager/scripts/validate_eval_suite.py skills/clarify/eval/eval-cases.json
• uv run python skills/skill-lifecycle-manager/scripts/validate_projection.py skills/clarify --platform all

If you changed the runtime projections, regenerate them from the canonical package instead of hand-editing the projected copies:

• uv run python skills/skill-lifecycle-manager/scripts/project_skill.py skills/clarify --platform all

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Anti-Patterns to Avoid

• Over-asking: Do not create a questionnaire when one decision is the only real blocker.
• Open-ended fumbling: Avoid "what do you want?" when you could offer concrete hypotheses.
• Premature implementation: Do not code through unresolved HIGH-risk contradictions.
• Ignoring evidence: Do not ask about things the repository, docs, or tests already answer.
• Analysis paralysis: LOW-risk ambiguity is often best handled with a stated assumption and forward motion.
• Brainstorming drift: Do not use clarification as a substitute for genuine option exploration or strategy work.