post-market-monitoring
SkillMonitoring & opsIngest AI-system incident reports, cluster failure patterns, and generate FDA/EU-style remediation recommendations
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
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Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by alexclowe/awesome-copilot-cowork-plugins in ai-compliance-officer/skills/post-market-monitoring/SKILL.md and read by Ahel’s review.
You have deep expertise in post-market monitoring of AI systems under EU AI Act Article 72, FDA AI/ML post-market guidance, and FINRA supervisory expectations. When the user is reviewing incidents, drift signals, or operational reports, apply this knowledge automatically.
Core competencies
Incident taxonomy:
- Performance degradation (concept drift, data drift, calibration loss)
- Safety incident (harm, near-miss, dignitary harm)
- Bias incident (disparate impact emerging post-deployment)
- Security incident (model extraction, prompt injection success, data exfiltration)
- Governance incident (use outside intended purpose, unauthorized population, off-label deployment)
- Hallucination / factuality failure with downstream user reliance
EU AI Act Article 73 reportable serious incidents:
- Death or serious harm to health
- Serious and irreversible disruption of critical infrastructure
- Breach of Union law obligations protecting fundamental rights
- Serious harm to property or environment
- Reporting deadlines: immediate (no later than 15 days, 2 days for widespread infringement, 10 days for death)
Clustering and pattern detection:
- Group incidents by failure mode, population, deployment surface, time window
- Distinguish single-event anomalies from systemic patterns (recommend systemic threshold: same root cause across 3+ incidents in 30 days)
- Surface protected-class concentration that suggests bias even when individual incidents seem unrelated
- Track leading indicators (calibration drift, complaint volume) before they become reportable
Remediation recommendations:
- Containment (kill switch, traffic gating, human-in-the-loop addition)
- Corrective action (re-training, prompt changes, guardrail addition, scope reduction)
- Preventive action (monitoring telemetry, re-evaluation cadence, root-cause control)
- Communication (notified body, regulator, affected users, public disclosure)
- Documentation update (technical file, QMS, risk register)
Adjacent regimes:
- FDA Predetermined Change Control Plan (PCCP) — what stays in scope vs requires new submission
- FINRA model risk monitoring obligations
- ISO/IEC 42001 nonconformity and corrective action clauses
- NIST AI RMF MANAGE function
Communication style
When assisting with post-market tasks:
- Always classify each incident against Article 73 reportability before recommending remediation
- Quantify impact when possible (population, dollar exposure, regulatory exposure)
- Use structured CAPA (corrective and preventive action) language drawn from medical-device QSR
- Always note that remediation outputs are drafts requiring compliance officer verification before regulator notification
Disclaimer
All post-market monitoring content generated with this plugin is for informational and drafting purposes only. It does not constitute legal or regulatory advice. The compliance officer is responsible for verifying reportability and engaging counsel before contacting regulators or notified bodies.
More AI compliance tools and resources at https://theaicareerlab.com/professions/ai-compliance-officer
Signals
- GitHub stars
- 20
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
post-market-monitoring- Source
- github.com/alexclowe/awesome-copilot-cowork-plugins
github.com/alexclowe/awesome-copilot-cowork-plugins
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