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SKILL verified MIT Self-run

Ai Assisted Development Governance

skill-aminalam-meddev-agent-skills-ai-assisted-development-governance · by AminAlam

A Claude skill from AminAlam/meddev-agent-skills.

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Install

$ agentstack add skill-aminalam-meddev-agent-skills-ai-assisted-development-governance

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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4mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

AI-Assisted Development Governance (LLM Coding Agents)

Purpose

Define governance for AI/LLM-assisted software development (e.g., Cursor, Copilot, Claude): human review expectations, evidence that generated code is covered by requirements-based tests, tool validation for GxP-adjacent workflows, and alignment with Computer Software Assurance (CSA) principles (risk-based, critical thinking) without replacing your SOPs.

When to Apply

  • Teams using LLMs for code generation, refactor, or test synthesis in regulated projects.
  • Establishing AI usage policies for design history / audit trail.
  • Qualifying IDE plugins or CI bots that touch production or QMS repositories.

Requirements (testable)

  1. Policy: Document where LLM assistance is allowed (e.g., scaffolding vs safety-critical modules) and required human review gates per risk class. Rationale: controlled process.
  2. Traceability: Every merged change must retain author/reviewer identity; LLM assistance disclosed in commit or PR template when policy requires. Rationale: audit trail.
  3. Independent Review: For Class B/C changes, second-person review of generated patches focusing on requirements coverage, failure modes, and misinterpreted specs. Rationale: defect detection beyond automation.
  4. Test Evidence: No merge without tests (or justified analysis) demonstrating REQ→TEST linkage for new behavior; forbid “LLM says it works” as evidence. Rationale: verification independence from generator.
  5. Prompt & Context Hygiene: No PHI/PII/proprietary trial data in prompts; use sanitized snippets; pin skill/context versions (e.g., SKILL.md hashes) when claims depend on agent guidance. Rationale: confidentiality and reproducibility.

5a. IEC 62304 Skills by Class: Load REG-IEC62304 and child skills appropriate to safety class (e.g. DOC-SW-REQ, REG-IEC62304-SWRM for B/C, detailed design for C); record skill IDs/versions in change records. Rationale: proportional lifecycle guidance.

  1. Tool Validation (GxP): For tools affecting GMP/quality records, apply FDA CSA approach: risk assessment, intended use, testing records, and change control when models or plugins update. Rationale: 21 CFR Part 11 / 820 alignment for QMS software.
  2. Regression & Flakes: Treat LLM-generated tests like any other code — deterministic CI, seed control where needed, flake triage. Rationale: valid CI as design verification aid.
  3. Record Retention: Store PR descriptions, review comments, and test reports as part of DHF/evidence; avoid relying on vendor chat logs as SOLE records. Rationale: durable evidence.

Recommended Practices

  • Use a PR checklist: [ ] REQ IDs, [ ] tests added, [ ] reviewer not sole prompt author, [ ] no secrets in diff.
  • Snapshot agent skill versions in release notes when agent guidance materially influenced design.

Patterns

PR template excerpt (markdown):

## AI assistance
- [ ] No PHI/secrets in prompts or logs
- [ ] Assistant tools: Cursor 2.x + skills @ commit `abc1234`
- [ ] Reviewer verified REQ/TEST mapping for generated code

Commit message:

fix: debounce door sensor per REQ-62304-088

AI: scaffolded by LLM; reviewed by @jdoe; TEST-412 added

Anti-Patterns (risks)

  • Blind merge of generated safety-critical code — risk: latent defects and audit failure.
  • Pasting production credentials into chat — risk: data breach and Part 11 issues.
  • Skipping review because “coverage is high” — risk: wrong tests for the right requirement.

Verification Checklist

  • [ ] AI use policy acknowledged for the repo/product line.
  • [ ] PRs show human reviewer distinct from sole prompt author where required.
  • [ ] Requirements and tests updated for all generated behavior changes.
  • [ ] No sensitive data in prompts or CI logs (spot audit).
  • [ ] Tool/plugin major updates assessed under change control if GxP applies.

Traceability

  • Link REQ-* / TEST-* / PR URLs in change records; optional tag AI-GEN on commits for metrics (internal only).

References

  • FDA Guidance: “Computer Software Assurance for Production and Quality System Software” (2022).
  • IEC 62304:2006+A1:2015 — software development process (human processes still apply).
  • Internal QMS SOPs for design control and records (primary authority).

Changelog

  • 1.0.1 (2026-05-21): Require class-appropriate IEC 62304 child skills and version pinning in change records.
  • 1.0.0 (2026-03-20): Initial skill for LLM-assisted dev governance, review, and CSA alignment.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

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Versions

  • v0.1.0 Imported from the upstream source.