change-traceability-review
openbooklet.com/s/change-traceability-reviewopenbooklet.com/s/[email protected]GET /api/v1/skills/change-traceability-reviewUse for change traceability review across specs, commits, PRs, branches, local diffs, and git history, including Spec Preparation, Review Readiness Checks, and Review Retrospectives, to generate or verify Story-linked specs, commit-message evidence, test evidence, risk signals, AI involvement, and review-flow improvement suggestions.
Use when /better-harness reviews the outer coding-agent Harness for lifecycle controls, repeated work, project feedback, agent assets, session outcomes, repair planning, durable reports, finding-bound fixes, or manual direct fixes. Invoke only via slash command.
Diagnose a bounded backend or multi-service failure from GitHub Issues, Jira, Aone, user-provided exports, logs, traces, responses, stack traces, or job records. Use when a service, API, RPC, worker, queue, CLI, or scheduled job bug needs correlation through the project's existing observability route before repair; do not use for frontend-only defects or generic logging reviews.
Use when bootstrapping or tightening the smallest harness-oriented skill from an existing repository, workflow, evaluation corpus, or harness-analysis chain. Trigger for meta skills, reusable harness workflows, Qoder/Codex plugin skill sets, skill blueprints, and harness-analysis-like skills that need evidence-backed references, validation gates, and qodercli forward-tests.
Build a bounded, replayable browser or UI bug reproduction from GitHub Issues, Jira, Aone, user-provided exports, screenshots, videos, comments, or attachments. Use for browser, WebView, IDE or desktop UI, interaction, responsive, rendering, accessibility, or visual defects that need evidence-preserving reproduction before diagnosis or repair; do not use for ordinary implementation or backend-only failures.
Review and improve architecture specs, ADRs, plugin or agent directory proposals, and other design documents by running multiple independent AI reviewers such as Claude, Qoder, Codex, or Cursor Agent against the same artifact, normalizing P1/P2/P3 findings, and iterating only on blocking or high-risk issues.
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