feat(skills): add FIPS 199 categorization review - #506

Adversarial draft review at eb8af75c90f310582a823446516fb8441e14b99e: this skill correctly reserves final categorization for authorized officials and keeps evidence sanitized, but High-Water-Mark Check and Procedure 4 present N/A < Low < Moderate < High and system objective impact = maximum final impact across applicable information types as the entire FIPS 199 computation. FIPS 199 also requires considering impacts to the system itself/mission service, not only enumerated information types; a system that handles only low-impact data but whose unavailable function has a moderate mission impact would be incorrectly reported Low. Amend the check to incorporate system/service impact scenarios and applicable agency tailoring before deriving the per-objective system result; supply a sanitized counterexample or test case. Keep the calculation clearly provisional and human-approved. The PR is still draft, and the checklist says human security review/label remains pending. Static review only; AI-assisted (OpenCode).

Verdict: REQUEST CHANGES

AI-assisted adversarial re-review by OpenCode (GPT 6.1), requested by @mogul, of #506 at eb8af75c90f310582a823446516fb8441e14b99e.

Blocking findings

1. P1 — The prior system-impact calculation blocker remains unresolved (.agents/skills/fips-199-categorization/SKILL.md:110–120, output contract at 136–137). This is the same head reviewed on October 26. The procedure still defines the system result solely as the maximum final impact across information types. That is a starting point, not a complete system categorization review: system/mission consequences and system-level adjustments must also be considered. A sanitized counterexample is two separately Low-confidentiality information types whose aggregation has Moderate disclosure consequences; independently maximizing the type values returns Low and misses the aggregate system impact. Require explicit system-level/aggregation and mission-impact checks, record justified adjustments and their human decision owners, and reflect them in the output and verification checklist. Add a sanitized example or test case demonstrating that type maxima alone are insufficient. The existing non-approval language is good and should remain.

2. P2 — N/A is presented as valid for every system security objective (SKILL.md:114–117). The unrestricted ordering N/A < Low < Moderate < High allows an all-N/A input to produce system C/I/A=N/A without a finding. FIPS 199 permits not-applicable confidentiality for information types, not integrity/availability; system objectives have a minimum Low value. Distinguish information-type values from system values, flag N/A integrity/availability rather than rank it, and enforce the system-level Low floor. Add a public-information example with information-type C=N/A and I/A=Low, for which the system C/I/A is Low/Low/Low.

Verification and limits

Read the complete three-file diff, head content with line numbers, previous formal review, repository rules, generated-index wiring, and CI results. The skills symlink correctly resolves the catalog/index path to .agents/skills. All seven reported head checks are successful, but schema/lint/index checks do not validate these categorization semantics. The new skill has no test-case file or worked counterexample. No files modified, dependencies installed, or live assessment data processed. Direct retrieval of the cited NIST PDF/publication page was blocked by sandbox connectivity; that source-retrieval limitation is not represented as a successful check. Reference: https://csrc.nist.gov/pubs/fips/199/final (Security Categorization Applied to Information Types / Information Systems).

This AI-assisted review does not replace the required human security review or make an authorization decision.