speckit-learn-clarify

name	speckit-learn-clarify
description	Like /speckit.clarify but includes concise explanations of why each clarification matters, pros/cons of options, and reasoning behind recommendations — designed to build senior-level thinking skills.
compatibility	Requires spec-kit project structure with .specify/ directory
metadata	{"author":"github-spec-kit","source":"learn:commands/clarify.md"}

User Input

$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, record the clarifications directly in the spec file, AND teach the user the engineering reasoning behind each clarification — why it matters, what tradeoffs exist, and why a particular option is recommended.

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 {SCRIPT} --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) — with learning context:

   • Present EXACTLY ONE question at a time.

   • Before the question, include a concise "Why this matters" block (2-3 sentences max) that explains:

     • What category/area this question addresses
     • Why leaving it unresolved creates risk (e.g., rework, misaligned tests, performance surprises, security gaps)
     • What kind of downstream decision this unlocks (architecture, data model, test strategy, etc.)

     Format:

     **Why this matters:** <category> — <concise explanation of risk and what this decision unlocks>
     

   • 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:

       Format: **Recommended:** Option [X] — <1-2 sentence reasoning explaining WHY this is the best choice given the project context>

     • Then render all options as a Markdown table with a Pros/Cons column:

     Option	Description	Pros / Cons
     A		✓ · ✗
     B		✓ · ✗
     C		✓ · ✗
     Short	Provide a different short answer (<=5 words)	(Include only if free-form alternative is appropriate)

     Keep each pro/con to one brief phrase — the goal is quick signal, not exhaustive analysis.

     • 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 explaining WHY this value/approach is appropriate>
     • Then output: Format: Short answer (<=5 words). You can accept the suggestion by saying "yes" or "suggested", or provide your own answer.

   • After the user answers:

     • If the user replies with "yes", "recommended", or "suggested", use your previously stated recommendation/suggestion as the answer.
     • Otherwise, validate the answer maps to one option or fits the <=5 word constraint.
     • If ambiguous, ask for a quick disambiguation (count still belongs to same question; do not advance).
     • Once satisfactory, record it in working memory (do not yet write to disk) and move to the next queued question.

   • Stop asking further questions when:

     • All critical ambiguities resolved early (remaining queued items become unnecessary), OR
     • User signals completion ("done", "good", "no more"), OR
     • You reach 5 asked questions.

   • Never reveal future queued questions in advance.

   • If no valid questions exist at start, immediately report no critical ambiguities.

5. Integration after EACH accepted answer (incremental update approach):

   • Maintain in-memory representation of the spec (loaded once at start) plus the raw file contents.
   • For the first integrated answer in this session:
     • Ensure a ## Clarifications section exists (create it just after the highest-level contextual/overview section per the spec template if missing).
     • Under it, create (if not present) a ### Session YYYY-MM-DD subheading for today.
   • Append a bullet line immediately after acceptance: - Q: <question> → A: <final answer>.
   • Then immediately apply the clarification to the most appropriate section(s):
     • Functional ambiguity → Update or add a bullet in Functional Requirements.
     • User interaction / actor distinction → Update User Stories or Actors subsection (if present) with clarified role, constraint, or scenario.
     • Data shape / entities → Update Data Model (add fields, types, relationships) preserving ordering; note added constraints succinctly.
     • Non-functional constraint → Add/modify measurable criteria in Non-Functional / Quality Attributes section (convert vague adjective to metric or explicit target).
     • Edge case / negative flow → Add a new bullet under Edge Cases / Error Handling (or create such subsection if template provides placeholder for it).
     • Terminology conflict → Normalize term across spec; retain original only if necessary by adding (formerly referred to as "X") once.
   • If the clarification invalidates an earlier ambiguous statement, replace that statement instead of duplicating; leave no obsolete contradictory text.
   • Save the spec file AFTER each integration to minimize risk of context loss (atomic overwrite).
   • Preserve formatting: do not reorder unrelated sections; keep heading hierarchy intact.
   • Keep each inserted clarification minimal and testable (avoid narrative drift).

6. Validation (performed after EACH write plus final pass):

   • Clarifications session contains exactly one bullet per accepted answer (no duplicates).
   • Total asked (accepted) questions ≤ 5.
   • Updated sections contain no lingering vague placeholders the new answer was meant to resolve.
   • No contradictory earlier statement remains (scan for now-invalid alternative choices removed).
   • Markdown structure valid; only allowed new headings: ## Clarifications, ### Session YYYY-MM-DD.
   • Terminology consistency: same canonical term used across all updated sections.

7. Write the updated spec back to FEATURE_SPEC.

8. Report completion (after questioning loop ends or early termination):

   • Number of questions asked & answered.
   • Path to updated spec.
   • Sections touched (list names).
   • Coverage summary table listing each taxonomy category with Status: Resolved (was Partial/Missing and addressed), Deferred (exceeds question quota or better suited for planning), Clear (already sufficient), Outstanding (still Partial/Missing but low impact).
   • If any Outstanding or Deferred remain, recommend whether to proceed to /speckit.plan or run /speckit.learn.clarify again later post-plan.
   • Suggested next command.

Behavior rules:

• If no meaningful ambiguities found (or all potential questions would be low-impact), respond: "No critical ambiguities detected worth formal clarification." and suggest proceeding.
• If spec file missing, instruct user to run /speckit.specify first (do not create a new spec here).
• Never exceed 5 total asked questions (clarification retries for a single question do not count as new questions).
• Avoid speculative tech stack questions unless the absence blocks functional clarity.
• Respect user early termination signals ("stop", "done", "proceed").
• If no questions asked due to full coverage, output a compact coverage summary (all categories Clear) then suggest advancing.
• If quota reached with unresolved high-impact categories remaining, explicitly flag them under Deferred with rationale.
• Learning tone: Write the "Why this matters" and pros/cons content as if mentoring a junior engineer — direct, practical, no fluff. Connect each explanation to real consequences (e.g., "this could mean rewriting the data layer later" rather than abstract statements).

Context for prioritization: $ARGUMENTS