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Obsidian-Owl

speckit-clarify

by Obsidian-Owl

The Open Platform for building Data Platforms. Ship faster. Stay compliant. Scale to Data Mesh.

0🍴 11📅 Jan 24, 2026

SKILL.md


name: speckit-clarify description: Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Memory Integration

Before Starting

Search for similar clarifications from prior features:

./scripts/memory-search "clarifications for {feature domain}"

After Completion

Save Q&A decisions:

./scripts/memory-save --decisions "Clarified for {feature}: {key decisions}" --issues ""

Constitution Alignment

This skill enforces project principles:

  • Testable Requirements: Every requirement must be unambiguous and testable
  • User-Focused: Clarifications prioritize user value over technical details

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).

    Taxonomy Categories:

    • Functional Scope & Behavior
    • Domain & Data Model
    • Interaction & UX Flow
    • Non-Functional Quality Attributes
    • Integration & External Dependencies
    • Edge Cases & Failure Handling
    • Constraints & Tradeoffs
    • Terminology & Consistency
    • Completion Signals
    • Misc / Placeholders
  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.
  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
      • Present your recommended option prominently at the top with clear reasoning
      • Format as: **Recommended:** Option [X] - <reasoning>
      • Then render all options as a Markdown table
    • 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.
    • 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.
  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.
    • Ensure a ## Clarifications section exists
    • Append a bullet line immediately after acceptance: - Q: <question>: A: <final answer>.
    • Then immediately apply the clarification to the most appropriate section(s)
  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.
  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
    • Suggested next command.

Handoff

After completing this skill:

  • Create plan: Run /speckit.plan to generate technical implementation plan

References

  • spec.md - Feature specification
  • .specify/memory/constitution.md - Project principles

Score

Total Score

70/100

Based on repository quality metrics

SKILL.md

SKILL.mdファイルが含まれている

+20
LICENSE

ライセンスが設定されている

+10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
最近の活動

1ヶ月以内に更新

+10
フォーク

10回以上フォークされている

+5
Issue管理

オープンIssueが50未満

+5
言語

プログラミング言語が設定されている

+5
タグ

1つ以上のタグが設定されている

+5

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