ai-driven-dev Shadow Areas
Analytically scans a written artifact for gaps the author has not addressed. Unlike iterative Q&A clarification, this skill reads the existing material and emits a structured report: each gap carries a category from a locked 7-category taxonomy, a 3-tier severity, and a direct-question probe the author can act on immediately.
Actions
| # | Action | Role | Input |
|---|---|---|---|
| 01 | detect |
Parse input, extract gaps, classify category and severity, emit probes | file path or inline text |
| 02 | render-report |
Render markdown grouped by category and sorted by severity, write report | gap list from detect |
| 03 | diff |
Load prior report, classify gaps as closed / still-open / newly-introduced | gap list from detect + prior report path |
Dispatch by context: with no prior report run detect then render-report; with one, run detect then diff.
Before running an action, read its file in actions/, not only the table or assets.
Transversal rules
- Never modify the source artifact.
- Every gap carries all three: a category, a severity, and a probe question.
- Every probe is a direct question ending with
?. - Categories and severities come from the locked sets in
references/locked-sets.json. - When zero blockers and zero majors remain, stamp the report
status: clean. - On re-runs, gaps are matched by category and snippet, never by question wording, so rephrasing a question never creates a spurious "newly introduced" gap.
References
references/categories.md: locked 7-category taxonomy with definition and example per category.references/severity-rubric.md: blocker / major / minor decision rules and examples.references/probe-style.md: direct-question form rules.references/locked-sets.json: machine-readable sets reused by the validator.
Assets
assets/report-template.md: report skeleton with header, per-category sections, andstatus: cleanblock.