mcp-tekna: Command for Claude Code

Checklist Purpose: "Unit Tests for English"

CRITICAL CONCEPT: Checklists are UNIT TESTS FOR REQUIREMENTS WRITING - they validate the quality, clarity, and completeness of requirements in a given domain.

NOT for verification/testing:

• ❌ NOT "Verify the button clicks correctly"
• ❌ NOT "Test error handling works"
• ❌ NOT "Confirm the API returns 200"
• ❌ NOT checking if code/implementation matches the spec

FOR requirements quality validation:

• ✅ "Are visual hierarchy requirements defined for all card types?" (completeness)
• ✅ "Is 'prominent display' quantified with specific sizing/positioning?" (clarity)
• ✅ "Are hover state requirements consistent across all interactive elements?" (consistency)
• ✅ "Are accessibility requirements defined for keyboard navigation?" (coverage)
• ✅ "Does the spec define what happens when logo image fails to load?" (edge cases)

Metaphor: If your spec is code written in English, the checklist is its unit test suite. You're testing whether the requirements are well-written, complete, unambiguous, and ready for implementation - NOT whether the implementation works.

User Input

$ARGUMENTS

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

Execution Steps

1. Setup: Run .specify/scripts/powershell/check-prerequisites.ps1 -Json from repo root and parse JSON for FEATURE_DIR and AVAILABLE_DOCS list.

   • All file paths must be absolute.
   • 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. Clarify intent (dynamic): Derive up to THREE initial contextual clarifying questions (no pre-baked catalog). They MUST:

   • Be generated from the user's phrasing + extracted signals from spec/plan/tasks
   • Only ask about information that materially changes checklist content
   • Be skipped individually if already unambiguous in $ARGUMENTS
   • Prefer precision over breadth

   Generation algorithm:

   1. Extract signals: feature domain keywords (e.g., auth, latency, UX, API), risk indicators ("critical", "must", "compliance"), stakeholder hints ("QA", "review", "security team"), and explicit deliverables ("a11y", "rollback", "contracts").
   2. Cluster signals into candidate focus areas (max 4) ranked by relevance.
   3. Identify probable audience & timing (author, reviewer, QA, release) if not explicit.
   4. Detect missing dimensions: scope breadth, depth/rigor, risk emphasis, exclusion boundaries, measurable acceptance criteria.
   5. Formulate questions chosen from these archetypes:
      • Scope refinement (e.g., "Should this include integration touchpoints with X and Y or stay limited to local module correctness?")
      • Risk prioritization (e.g., "Which of these potential risk areas should receive mandatory gating checks?")
      • Depth calibration (e.g., "Is this a lightweight pre-commit sanity list or a formal release gate?")
      • Audience framing (e.g., "Will this be used by the author only or peers during PR review?")
      • Boundary exclusion (e.g., "Should we explicitly exclude performance tuning items this round?")
      • Scenario class gap (e.g., "No recovery flows detected—are rollback / partial failure paths in scope?")