Regulatory Review

Regulatory Review Skill

Assess whether the product falls under AI regulation and identify compliance requirements.

Preflight: Read target canvas file(s) before any Write/Edit

Hard rule. Before issuing Write or Edit against any .claude/canvas/*.yml, use the Read tool on that file in this session. Claude Code's Read-before-Write check requires the Read tool specifically — cat/head/grep via Bash do NOT satisfy it.

Edit vs Write — different cost profiles (verified 2026-05-14):

• Edit (exact-string replacement): Read with limit: 1 satisfies the check at ~50 tokens. State-tracking is per-file, not per-byte — subsequent Edit calls work anywhere in the file. Use this for partial updates against large canvas files (e.g., purpose.yml at 800+ lines).
• Write (full replacement): do a full Read first. Write obliterates the file; you should see what you're about to replace. The limit:1 shortcut is not appropriate here.

ID-bearing entries — scan the ID space before assigning (added 2026-05-15, v0.23.19): When adding a new component, opportunity, solution, or any other ID-bearing entry to a canvas file, run a Bash grep first to confirm the next ID in your prefix sequence is actually free:

grep "^  - id: <prefix>-" .claude/canvas/<file>.yml | sort -u

Replace <prefix> with the canvas's ID prefix (comp for landscape, opp for opportunities, sol for solutions, ht for human-tasks, etc.). Then pick the next free integer. validate_canvas.py has a duplicate-ID check (lines 230-239) that catches the failure on CI, but a duplicate can persist in the working tree for days if CI isn't run between edit and discovery — see roadmap-repo corrections.md 2026-05-15 "Duplicate canvas ID created in landscape.yml" for the worked example.

Original failure mode: anti-pattern #7 instance #5, 2026-05-09 — agent conflated Bash head with the Read tool, lost ~14k tokens to a Write-fail → remedial-full-Read → re-Write loop. The limit:1 discipline (graduated 2026-05-14, v0.23.18) prevents the second-order cost where the agent correctly follows the rule but full-Reads every time. The ID-scan discipline (graduated 2026-05-15, v0.23.19) prevents the related class where the agent reads enough of the file to satisfy the Edit check but not enough to see existing ID assignments — kin to anti-pattern #8 (Stale State Read).

If this skill writes to multiple canvas files, register each one first (limit:1 for Edit-only paths; full Read for Write paths) AND ID-scan any prefix you intend to assign.

See CLAUDE.md Canvas writes — Read before Write for the canonical rule.

When to Use

• At L3 Define->Develop and Develop->Deliver transitions (Regulatory Gate)
• At L4 Develop->Deliver when product_type is ai_tool
• At L5 Develop->Deliver for any product with user-facing AI features
• When guardrails G-S7 or G-S8 are triggered

Workflow

1. Determine AI presence: Does this product contain AI/ML components?

   • If no: document "No AI components -- Regulatory Gate N/A" and stop
   • If yes: proceed to classification

2. EU AI Act risk classification (Regulation 2024/1689):

   • Check Annex III high-risk categories:
     • Biometric identification/categorization
     • Critical infrastructure management
     • Education and vocational training (access, assessment)
     • Employment (recruitment, screening, evaluation)
     • Essential services (credit scoring, insurance, social benefits)
     • Law enforcement
     • Migration and border control
     • Justice and democratic processes
   • Classify risk level: Minimal / Limited / High / Unacceptable

3. Article 50 transparency check (applies from 2 August 2026):

   • Does the system interact directly with people? -> Must disclose AI nature
   • Does it generate synthetic content? -> Must machine-mark outputs
   • Does it generate deepfakes? -> Must disclose artificial generation

4. Product-type-specific checks:

   • ai_tool: Full classification required. Check eval results for bias, safety review for harm potential.
   • software: Check if any feature uses AI/ML (recommendations, search, classification). If yes, assess that feature.
   • content_*: Check if AI generates or curates content shown to users. If yes, transparency disclosure needed.
   • service_offering: Check if AI assists in service delivery decisions. If yes, explainability required.

5. Document findings:

   • Update .claude/canvas/threat-model.yml with regulatory classification
   • Update .claude/canvas/privacy-assessment.yml if data processing is involved
   • Log classification decision in .claude/harness/decision-log.md

6. Determine compliance path:

   • Minimal risk: No action required beyond documentation
   • Limited risk: Transparency obligations (Article 50)
   • High risk: Conformity assessment path must be planned before L4 delivery
   • Unacceptable risk: Product cannot proceed in current form

Output Format

## Regulatory Review: [Product Name]

AI Components: [Yes/No]
Risk Classification: [Minimal/Limited/High/Unacceptable]

### Annex III Assessment
| Category | Applicable? | Rationale |
|----------|------------|-----------|
| Biometric | No | ... |
| Critical infra | No | ... |
| ... | ... | ... |

### Transparency Requirements
- AI disclosure needed: [Yes/No]
- Synthetic content marking: [Yes/No]
- Deepfake disclosure: [Yes/No]

### Compliance Path
[What needs to happen before delivery]

### Decision
Regulatory Gate: [Pass/Fail/N-A]

Important Disclaimer

Mycelium is not a legal compliance tool. This skill raises awareness and prompts assessment of regulatory applicability. For actual compliance decisions, consult qualified legal counsel specializing in AI regulation.

Theory Citations

• EU AI Act (Regulation 2024/1689)
• Article 50 transparency obligations
• Annex III high-risk categories