Let super-skills surface findings the agent identifies on its own,
clearly tagged so consumers can render them differently from
knowledge-backed ones.
- skills/do.md: permit references:[] when from-sub-skill='agent';
define the agent-finding encoding (id 'agent:<slug>', confidence
capped at medium, self-contained message); restrict agent findings
to super-skills only.
- microsoft/skills/review/al-code-review.md: add a self-review pass
to Action that validates agent-identified candidates against
BCQuality (cite if matched, suppress if contradicted, surface as
agent finding otherwise). Add example finding.
- agent-consumption.md, README.md: describe the additive model and
the from-sub-skill: 'agent' marker so consumer orchestrators know
to render unbacked findings.
Strictly additive: existing knowledge-backed flow is unchanged and
backward compatible.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
The knowledge corpus now covers performance, security, privacy, upgrade,
style, and UI. Previously only two leaf reviewer skills existed
(al-performance-review, al-security-review), so four of the six domains
had knowledge with no skill sourcing from them. A community reader
landing in privacy/, upgrade/, style/, or ui/ would see articles with
no apparent consumer.
Three changes:
1. Move existing review skills into `microsoft/skills/review/`. The
`review/` subfolder groups all review-kind skills together and leaves
room for future non-review action skills at the `microsoft/skills/`
level. Updates references in README.md, agent-consumption.md, and
skills/entry.md to the new paths.
2. Add four new leaf reviewer skills — al-privacy-review,
al-upgrade-review, al-style-review, al-ui-review — each following
the same DO template as al-performance-review/al-security-review but
sourcing from the corresponding knowledge domain. al-upgrade-review
and al-ui-review return `not-applicable` when the diff contains no
upgrade surface or no page files, respectively.
3. Update al-code-review to compose all six leaf skills and retarget
the dangling references in every populated JSON example
(`use-setloadfields.md`, `no-plaintext-secrets-in-telemetry.md`,
`avoid-implicit-commit.md` — none of which exist in the corpus) to
real knowledge files: `call-setloadfields-before-filters.md`,
`use-secrettext-for-credentials.md`, `never-hardcode-secrets-in-al.md`.
Validator passes with 0 errors / 0 warnings.
