plugins/spec-kitty-plugin/skills/spec-kitty-clarify/SKILL.md
Identify underspecified areas in the current feature spec by asking up
npx skillsauth add richfrem/agent-plugins-skills spec-kitty-clarifyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill requires Python 3.8+ and standard library only. No external packages needed.
To install this skill's dependencies:
pip-compile ./requirements.in
pip install -r ./requirements.txt
See ./requirements.txt for the dependency lockfile (currently empty — standard library only).
$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
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 /spec-kitty.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:
Run spec-kitty agent feature check-prerequisites --json --paths-only from the repository root and parse JSON for:
feature_dir - Absolute path to feature directory (e.g., /path/to/kitty-specs/017-my-feature/)FEATURE_SPEC - Absolute path to spec.md filetarget_branch / base_branch (deterministic branch contract)/spec-kitty.specify first or verify they are in a spec-kitty-initialized repository.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:
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:
For each category with Partial or Missing status, add a candidate question opportunity unless:
Generate (internally) a prioritized queue of candidate clarification questions (maximum 5). Do NOT output them all at once. Apply these constraints:
Sequential questioning loop (interactive):
Options: (A) describe option A · (B) describe option B · (C) describe option C · (D) short custom answer (<=5 words)Format: Short answer (<=5 words).Integration after EACH accepted answer (incremental update approach):
## Clarifications section exists (create it just after the highest-level contextual/overview section per the spec template if missing).### Session YYYY-MM-DD subheading for today.- Q: <question> → A: <final answer>.(formerly referred to as "X") once.Validation (performed after EACH write plus final pass):
## Clarifications, ### Session YYYY-MM-DD.Write the updated spec back to FEATURE_SPEC.
Report completion (after questioning loop ends or early termination):
/spec-kitty.plan or run /spec-kitty.clarify again later post-plan.Behavior rules:
/spec-kitty.specify first (do not create a new spec here).Context for prioritization: User arguments from $ARGUMENTS section above (if provided). Use these to focus clarification on specific areas of concern mentioned by the user.
tools
Ingests repository files into the ChromaDB vector store. Builds or updates the vector index from a manifest or directory scan using ingest.py. Use when new files need to be indexed or the vector store is out of date. <example> user: "Index these new plugin files into the vector database" assistant: "I'll use vector-db-ingest to add them to the vector store." </example> <example> user: "The vector store is missing recent files -- update it" assistant: "I'll use vector-db-ingest to re-index the changes." </example>
data-ai
Removes stale and orphaned chunks from the ChromaDB vector store for files that have been deleted or renamed. Use after files are removed or moved to keep the vector index in sync with the filesystem. <example> user: "Clean up the vector store after I deleted some files" assistant: "I'll use vector-db-cleanup to remove orphaned chunks." </example> <example> user: "The vector database has chunks for files that no longer exist" assistant: "I'll run vector-db-cleanup to prune them." </example>
testing
Audit Vector DB coverage -- compares the live filesystem manifest against the ChromaDB index to identify coverage gaps.
development
3-Phase Knowledge Search strategy for the RLM Factory ecosystem. Auto-invoked when tasks involve finding code, documentation, or architecture context in the repository. Enforces the optimal search order: RLM Summary Scan (O(1)) -> Vector DB Semantic Search -> Grep/Exact Match. Never skip phases.