Audit SDD artifacts, requirement-task traceability, surface selection, and completion evidence before implementation or final delivery.
How this skill is triggered — by the user, by Claude, or both
Slash command
/spec-driven-development:sdd-auditThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use for SDD reviews, consistency checks, pre-implementation gates, completion proof, or questions like "is this spec ready", "are tasks traceable", or "can we claim done".
Use for SDD reviews, consistency checks, pre-implementation gates, completion proof, or questions like "is this spec ready", "are tasks traceable", or "can we claim done".
python3 <plugin>/scripts/sdd_surface_audit.py <repo> --json
python3 <plugin>/scripts/sdd_traceability_check.py <repo> --json
Use --feature-dir <path> when active feature is not inferable.
FAIL: missing core artifact, unresolved high-impact clarification, buildable requirement without task, completed task without evidence, or LLM self-judgment used as proof.WARN: weak traceability, vague requirement, missing optional design detail, missing non-critical evidence ledger, or manual-only proof.PASS: artifacts are present and traceable enough for the chosen lane.## SDD Audit
- STATUS: PASS | WARN | FAIL
- LANE: <detected or selected lane>
- FEATURE_DIR: <path or none>
- BLOCKING_FINDINGS: <count and top findings>
- WARNINGS: <count and top findings>
- NEXT: <specific action>
Do not rewrite artifacts during an audit unless the user explicitly asks for fixes.
npx claudepluginhub xopoko/plug-n-skills --plugin spec-driven-developmentProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.