feat: add GxP AI governance coverage (#348)

* feat: add GxP AI governance coverage * fix: complete GxP governance attribution and evaluation coverage * fix: complete GxP provenance references * fix: expose GxP routing and complete research provenance * chore: regenerate marketplace metadata * chore: regenerate marketplace metadata * chore: regenerate llms skill catalog
2026-09-11 19:47:12 +03:00 · 2026-08-20 22:48:41 -04:00
parent 3d8ad446b9
commit f73162d176
10 changed files with 394 additions and 13 deletions
@@ -75,7 +75,7 @@
        "./ai-governance"
      ],
      "strict": false,
-      "description": "Design and operate an organization's AI governance system: define governance principles, operating models and decision rights, risk frameworks, lifecycle gates, and fairness, transparency, privacy, security, regulatory, and board-oversight controls. Use when standing up a governance program, tiering AI use-case risk, reviewing an LLM or agent system for governance and safety gaps, mapping a regulation to a compliance plan, scoring governance maturity, or preparing board reporting. Do not use for interpreting regulations as legal advice (route to legal-strategy), data-governance mechanics (data-architect/data-engineering), or implementing application security (secure-software-engineering)."
+      "description": "Design and operate an organization's AI governance system: define governance principles, operating models and decision rights, risk frameworks, lifecycle gates, and fairness, transparency, privacy, security, regulatory, and board-oversight controls. Use when standing up a governance program, tiering AI use-case risk, reviewing an LLM or agent system for governance and safety gaps, mapping a regulation to a compliance plan, scoring governance maturity, or preparing board reporting. For regulated life-sciences use cases, also cover GxP, ALCOA+, data integrity, electronic records, validation/assurance, and QMS interfaces. Do not use for interpreting regulations as legal advice (route to legal-strategy), data-governance mechanics (data-architect/data-engineering), or implementing application security (secure-software-engineering)."
    },
    {
      "name": "anydoc",
@@ -17,7 +17,7 @@ After installing, your agent can stand up a governance program from scratch, tie
 risk and prescribe the controls each one requires, review an LLM or agent system for governance
 and safety gaps, map a regulation to a concrete compliance and control plan, score organizational
 governance maturity and get a prioritized gap list, and prepare board-level reporting. It ships
-dense references for each governance domain, six fillable templates, and two executable scripts,
+dense references for each governance domain, including a GxP/data-integrity overlay, six fillable templates, and two executable scripts,
 so the method turns into working artifacts instead of advice.
 ## What You Get
@@ -25,7 +25,7 @@ so the method turns into working artifacts instead of advice.
 | Path | What it provides |
 |---|---|
 | `SKILL.md` | The router: triggers, what the skill owns vs. doesn't, and when to load each file |
-| `references/` (11 files) | Dense, scannable guides: principles, operating model, risk frameworks, lifecycle, fairness, transparency, privacy, LLM/agent security, regulation, procurement/board oversight, and a source index |
+| `references/` (12 files) | Dense, scannable guides: principles, operating model, risk frameworks, lifecycle, fairness, transparency, privacy, LLM/agent security, regulation, procurement/board oversight, source index, and a GxP/data-integrity overlay |
 | `templates/` (6 files) | Fillable artifacts: governance charter, use-case intake, model risk assessment, model card, third-party due diligence, board report |
 | `scripts/governance-maturity.py` | CLI that scores an organization's governance maturity from JSON answers and lists gaps |
 | `scripts/use-case-risk-tier.py` | CLI that classifies an AI use case into a risk tier and its required controls |
@@ -62,6 +62,7 @@ Load this skill when you or your agent need to:
 - Map a current regulation to a compliance and control plan.
 - Score organizational governance maturity and prioritize gaps.
 - Prepare board-level AI governance reporting or run third-party/model due diligence.
 - Govern AI used in a GxP context, including ALCOA+, data integrity, electronic records, validation/assurance, audit trails, or QMS interfaces.
 ## Requirements
@@ -7,7 +7,9 @@ description: >-
  board-oversight controls. Use when standing up a governance program, tiering
  AI use-case risk, reviewing an LLM or agent system for governance and safety
  gaps, mapping a regulation to a compliance plan, scoring governance maturity,
-  or preparing board reporting. Do not use for interpreting regulations as legal
+  or preparing board reporting. For regulated life-sciences use cases, also cover GxP,
  ALCOA+, data integrity, electronic records, validation/assurance, and QMS interfaces.
  Do not use for interpreting regulations as legal
  advice (route to legal-strategy), data-governance mechanics
  (data-architect/data-engineering), or implementing application security
  (secure-software-engineering).
@@ -38,6 +40,7 @@ manual and not legal or security advice.
 | Lifecycle stage gates across ideation, build, evaluate, deploy, monitor, retire | CI/CD pipeline and deployment-gate configuration |
 | Fairness, bias, transparency, explainability, and accountability controls | Product portfolio/roadmap governance cadences |
 | Privacy and data governance for training and operational data | Capital allocation, org structure, or M&A governance |
 | GxP AI governance overlay: ALCOA+, data integrity, electronic records, risk-based assurance, QMS interfaces | Legal applicability determinations, validation protocols, SOPs, or quality-system operation |
 | LLM/agent safety: prompt injection, excessive agency, red-teaming, supply chain | Host-level or application-level security scanning |
 | Regulatory landscape and compliance mapping (as guidance, not advice) | Legal drafting, regulatory filings, or attorney-client work product |
 | Third-party and model due diligence, board reporting, audit | Any authoritative statement of "your system is compliant" |
@@ -67,6 +70,7 @@ Progressive disclosure: load only the reference relevant to the current question
 | Fairness metrics and their limits, bias sources, trade-offs, algorithmic justice | [references/fairness-bias-accountability.md](references/fairness-bias-accountability.md) |
 | Explainability (XAI) methods, when explanation is required, disclosure, auditability | [references/transparency-and-explainability.md](references/transparency-and-explainability.md) |
 | Training/operational data governance, ownership, lineage, quality, consent, PETs | [references/privacy-and-data-governance.md](references/privacy-and-data-governance.md) |
 | AI used in GLP, GCP, GMP, GDP, or pharmacovigilance contexts; ALCOA+, data integrity, electronic records, audit trails, validation/assurance, and QMS interfaces | [references/gxp-and-data-integrity.md](references/gxp-and-data-integrity.md) |
 | Trust boundaries, prompt injection, excessive agency, hallucination, supply chain, red-teaming | [references/llm-and-agent-security.md](references/llm-and-agent-security.md) |
 | Current law by jurisdiction, compliance mapping, enforcement, horizon scanning | [references/regulatory-landscape.md](references/regulatory-landscape.md) |
 | Vendor/model due diligence, supply chain, board reporting, metrics, audit | [references/procurement-third-party-and-board-oversight.md](references/procurement-third-party-and-board-oversight.md) |
@@ -102,7 +106,8 @@ changing anything. Exit 0 on success; the maturity scorer also exits 1 on a crit
 - **Eval manifest:** [evals/evals.json](evals/evals.json) holds the output-quality cases (operating
  model design, use-case risk tiering, LLM-app governance review, fairness/accountability review,
-  regulatory compliance mapping, board governance reporting) used to grade this skill.
+  regulatory compliance mapping, board governance reporting, and GxP/data-integrity governance)
  used to grade this skill.
 - **Configuration:** [pytest.ini](pytest.ini) overrides the repository's root coverage settings so
  the subprocess-based skill tests run cleanly; do not add a second override.
 - **Entry points:** this [SKILL.md](SKILL.md) is the router; [README.md](README.md) is the
@@ -69,6 +69,21 @@
        "documents the escalation path to the board or risk committee",
        "structures the report so directors can exercise oversight and ask pointed questions"
      ]
    },
    {
      "id": "gxp-ai-data-integrity-overlay",
      "prompt": "A pharmaceutical company wants to use an AI system to summarize laboratory results and flag potential out-of-specification investigations. Design the governance overlay before pilot and release. Cover GxP applicability, ALCOA+ data integrity, electronic records and audit trails, risk-based validation or assurance, AI-specific evidence, QMS interfaces, supplier controls, monitoring, change control, and retirement. Do not claim legal compliance; distinguish AI governance from QA, validation, and regulatory responsibilities.",
      "expected_output": "A bounded GxP AI governance plan that connects the AI lifecycle to the quality system, explicitly evaluates ALCOA+ attributes and electronic-record/audit-trail controls, defines a risk-based validation or assurance evidence plan, adds AI-specific context-of-use and performance evidence, assigns QA/QMS/validation/data/system owners, and defines monitoring, change, deviation/CAPA, supplier, and retirement controls without presenting legal advice as a compliance conclusion.",
      "assertions": [
        "screens GxP applicability and records the system boundary, domain, intended use, and accountable owners",
        "explicitly covers ALCOA+ rather than treating lineage and provenance as sufficient",
        "addresses electronic records, signatures, audit trails, access, time, retention, and retrieval as applicable",
        "defines risk-based validation or assurance evidence including context of use, acceptance criteria, traceability, failure cases, and change triggers",
        "connects AI governance to QA/QMS processes such as release, deviation, CAPA, change control, monitoring, periodic review, and retirement",
        "states that the plan is guidance and does not determine legal applicability or certify compliance",
        "includes supplier/model controls such as provider change notification, evidence access, continuity, and exit planning",
        "includes AI-specific performance evidence such as context of use, representativeness, generalizability, uncertainty, or drift"
      ]
    }
  ]
 }
@@ -0,0 +1,164 @@
 # GxP AI Governance and Data Integrity
 Use this reference when an AI system creates, transforms, analyzes, or influences GxP-relevant data, records, processes, decisions, or regulatory submissions. It adds a GxP quality-system overlay to the general AI governance method.
 This is governance guidance, not legal advice, a validation package, a QMS replacement, or a declaration that a system is compliant. Confirm applicability and current requirements with the organization's QA/quality unit, regulatory, validation/assurance, privacy, security, and legal owners.
 ## The governing boundary
 General AI governance answers:
 - Who owns the AI system and its risks?
 - What is the intended use and risk tier?
 - What evidence gates development, release, monitoring, change, and retirement?
 - How are model, data, supplier, security, fairness, and performance risks managed?
 The GxP quality system additionally answers:
 - Does the system create, modify, transmit, retain, or rely on regulated records or data?
 - Which GxP domain and jurisdiction apply: GLP, GCP, GMP, GDP, or pharmacovigilance?
 - What data-integrity attributes and electronic-record controls are required?
 - What validation or computer-software-assurance evidence is proportionate to risk?
 - How do deviations, CAPA, change control, training, periodic review, and quality-unit decisions operate?
 Do not create a parallel AI bureaucracy. Connect AI governance decisions to the authoritative QMS, controlled records, validation repository, supplier-quality process, and incident/deviation system.
 ## GxP applicability screen
 At intake, record:
 - GxP domain(s), product/process, jurisdiction, and regulated business owner.
 - Whether the AI system is used in discovery, clinical research, laboratory work, manufacturing, quality control, distribution, safety/pharmacovigilance, or regulatory submission support.
 - Whether it creates, modifies, interprets, approves, transmits, or stores GxP-relevant records or data.
 - Whether an AI output can affect patient safety, product quality, batch disposition, study integrity, subject safety, or a regulatory decision.
 - System boundary: model, prompts, retrieval sources, data pipelines, human review, downstream systems, hosted services, and retained records.
 - Named process owner, system owner, data owner, QA/quality-unit reviewer, validation/assurance lead, and AI-governance reviewer.
 - Applicable internal procedures and external requirements, with a primary-source verification owner and review date.
 If the system does not touch GxP-relevant processes or records, document why the GxP overlay is out of scope instead of silently assuming it.
 ## ALCOA+ data-integrity review
 Use ALCOA+ as a control review, not as a slogan. For every GxP-relevant record or dataset, identify the control and evidence for each attribute:
 | Attribute | Governance question |
 |---|---|
 | Attributable | Can the person, system, or agent responsible for each action be identified? |
 | Legible | Can authorized users read and interpret the record throughout its retention period? |
 | Contemporaneous | Is the record captured when the activity occurs, with reliable time handling? |
 | Original | Is the source record preserved, or is the relationship to the original demonstrable? |
 | Accurate | Are the record, transformation, calculation, and output correct and checked? |
 | Complete | Are relevant data, metadata, failed runs, exceptions, changes, and audit-trail events retained? |
 | Consistent | Are sequence, timestamps, formats, units, and meanings coherent across systems? |
 | Enduring | Will the record remain intact and usable for the required retention period? |
 | Available | Can authorized users retrieve the record, metadata, and audit history when needed? |
 For AI, extend the review across the full chain: source data, labeling or curation, prompt/context, retrieval results, model/version, parameters and configuration, output, human review, downstream action, and retained evidence. A polished answer is not an acceptable substitute for the underlying record.
 ## Electronic records, signatures, and audit trails
 When the system creates or relies on regulated electronic records, the gate must explicitly assess the applicable electronic-record and signature controls. Do not infer applicability from the presence of a model alone.
 Confirm, as applicable:
 - Validation or assurance evidence supports the intended use and critical functions.
 - Access is limited to authorized individuals and service identities; privileges are reviewed.
 - Records are protected against unauthorized alteration and remain accurately retrievable.
 - Audit trails capture time-sequenced creation, modification, deletion, and relevant configuration or model changes.
 - Audit trails are enabled, protected, periodically reviewed, and linked to investigations or deviations when needed.
 - Electronic signatures identify the signer, bind the signature to the record, and cannot be repudiated casually.
 - Time sources, time zones, clock changes, and synchronization are controlled and documented.
 - The retention, archival, backup, restoration, export, and readability plan covers records plus relevant metadata and audit history.
 - AI-generated or AI-assisted content is distinguishable from human review and approval where the process requires that distinction.
 These are control prompts, not a legal conclusion about 21 CFR Part 11, EU GMP Annex 11, or another regime. Have the responsible quality and regulatory functions determine which requirements apply.
 ## Risk-based validation and assurance
 Use a documented, risk-based validation or computer-software-assurance decision. The question is not “is AI validated?” in the abstract. The question is whether the system is fit for its intended GxP use and whether the evidence is proportionate to the risk.
 The evidence plan should state:
 1. **Intended use and context of use.** What the system may and may not do, who uses it, and what decisions it can influence.
 2. **Criticality and risk.** Impact on patient safety, product quality, subject rights, study integrity, data integrity, and regulatory submissions.
 3. **Requirements and acceptance criteria.** Functional, data-integrity, performance, security, human-oversight, and record-retention requirements agreed before testing.
 4. **Traceability.** Links among requirements, risk controls, tests, results, deviations, approvals, and the released system/model/data versions.
 5. **Test evidence.** Representative data, boundary and failure cases, relevant subgroups, abnormal inputs, uncertainty, fallback behavior, and human-review effectiveness.
 6. **Release decision.** Named approver, QA/quality-unit involvement where required, unresolved deviations, residual risk, and operating restrictions.
 7. **Change boundaries.** What changes require impact assessment, regression testing, revalidation or re-assurance, retraining review, or a new approval.
 8. **Retirement and continuity.** Record retention, reproducibility, migration, rollback, decommissioning, and access to historical outputs and evidence.
 For probabilistic, adaptive, or generative systems, add controls for nondeterminism, model/provider changes, prompt and retrieval changes, data drift, version pinning, output review, and the risk that a vendor changes behavior without the organization's approval.
 ## GxP overlay on AI lifecycle gates
 | Gate | Minimum GxP questions and evidence |
 |---|---|
 | Intake / ideation | GxP applicability, domain, intended use, system boundary, criticality, owners, initial risk, and QMS route |
 | Data | ALCOA+ review, provenance, authorized use, source and transformation history, quality, representativeness, retention, and access controls |
 | Build / configure | Controlled versions of code, model, prompts, retrieval, data, configuration, and infrastructure; documented deviations and decisions |
 | Evaluate / validate | Approved requirements, risk-based assurance plan, traceability, representative and edge-case testing, failure handling, human oversight, and evidence review |
 | Release | QA/quality-unit decision as required, approved residual risk, complete records and audit trails, training, SOP/work-instruction updates, monitoring, rollback, and incident/deviation routes |
 | Operate / monitor | Performance and data-integrity monitoring, audit-trail review, drift and vendor-change monitoring, periodic review, access review, incidents, deviations, CAPA, and escalation |
 | Change | Impact assessment, change control, version and provenance update, regression/revalidation decision, approvals, and retained comparison evidence |
 | Retire | Controlled decommissioning, retention and retrieval, archival integrity, migration or destruction evidence, supplier exit, and closure of open risks or CAPA |
 ## QMS and accountability interfaces
 The governance record should identify which system owns each decision:
 | Decision or event | Primary accountable function | AI-governance contribution |
 |---|---|---|
 | GxP applicability and process impact | Process owner with QA/quality unit | Ensure AI use case, boundaries, and risk are recorded |
 | Validation/assurance strategy | Validation/assurance lead and system owner | Set AI-specific evidence and change questions |
 | Data integrity controls | Data owner, system owner, and QA | Apply ALCOA+ across data and model lineage |
 | Release for regulated use | Authorized quality and business approvers | Confirm governance gates and residual-risk record |
 | Deviation, incident, or suspected integrity failure | QMS/quality process owner | Escalate AI-specific evidence and preserve affected artifacts |
 | CAPA and change control | QMS/quality process owner | Ensure model, data, prompt, vendor, and configuration changes are in scope |
 | Supplier/model due diligence | Procurement and supplier quality | Require provenance, audit rights, change notification, service continuity, and evidence access |
 | Periodic review | System/process owner with QA | Recheck intended use, performance, integrity, access, drift, and changes |
 ## Supplier and hosted-model controls
 For third-party models, hosted APIs, retrieval services, labeling vendors, or cloud systems, document:
 - Model, provider, service, and dependency identity, version, location, and change-notification mechanism.
 - Provider evidence about development data, evaluation, limitations, security, availability, incident handling, and business continuity.
 - Contractual rights and practical ability to obtain records, audit trails, logs, evidence, and timely incident information.
 - How provider updates, model substitutions, prompt changes, safety-filter changes, outages, and data-location changes trigger impact assessment.
 - Whether the service permits retention, deletion, access restriction, and export of GxP-relevant prompts, inputs, outputs, metadata, and audit history.
 - Exit, rollback, migration, and record-retention plans that do not depend on indefinite vendor availability.
 ## Verification checklist
 - [ ] GxP domain, jurisdiction, intended use, and system boundary are documented.
 - [ ] Named process, system, data, quality, validation, and AI-governance owners exist.
 - [ ] ALCOA+ review covers source data through retained AI output and human action.
 - [ ] Electronic-record, signature, audit-trail, time, access, and retention controls are assessed where applicable.
 - [ ] Risk-based validation/assurance strategy and acceptance criteria were approved before testing.
 - [ ] Model, data, prompt, retrieval, configuration, and provider versions are traceable.
 - [ ] Testing covers representative use, failure modes, uncertainty, generalizability, and human oversight.
 - [ ] Release, monitoring, change, deviation/CAPA, periodic-review, rollback, and retirement routes are connected to the QMS.
 - [ ] Supplier changes and evidence-access limitations are controlled.
 - [ ] Current primary sources and internal procedures were re-verified by the responsible functions.
 ## Primary sources and implementation references
 Accessed 2026-08-20 UTC. Re-verify current versions and applicability at use time.
 - FDA, [Data Integrity and Compliance With Drug CGMP](https://www.fda.gov/media/119267/download)
 - FDA, [Part 11, Electronic Records; Electronic Signatures — Scope and Application](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application)
 - eCFR, [21 CFR Part 11](https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11)
 - MHRA, [Guidance on GxP Data Integrity](https://www.gov.uk/government/publications/guidance-on-gxp-data-integrity)
 - PIC/S, [PI 041-1 Guidance on Data Integrity](https://picscheme.org/docview/4234)
 - WHO, [TRS 1033 Annex 4: Guideline on Data Integrity](https://www.who.int/docs/default-source/medicines/norms-and-standards/guidelines/inspections/trs1033-annex4-guideline-on-data-integrity.pdf)
 - EMA, [Guideline on Computerised Systems and Electronic Data in Clinical Trials](https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf)
 - EMA, [Reflection Paper on AI in the Medicinal Product Lifecycle](https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf)
 - FDA, [Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological)
 - ICH, [Q9(R1) Quality Risk Management](https://database.ich.org/sites/default/files/ICH_Q9(R1)_Guideline_Step4_2022_1219.pdf)
 - ISPE, [GAMP 5 Guide, 2nd Edition](https://ispe.org/publications/guidance-documents/gamp-5-guide-2nd-edition)
 - European Commission, [EudraLex Volume 4, Annex 11: Computerised Systems](https://health.ec.europa.eu/system/files/2016-11/annex11_01-2011_en_0.pdf)
 ### Synthesized from
 This reference synthesizes the official regulator, inspectorate, standards-body, and industry sources listed above. The durable research basis is recorded in `../research/gxp-ai-governance-brief.md` and `../research/gxp-ai-governance-log.md`. The material is an original governance synthesis, not legal advice, a validation protocol, or a certification of compliance.
@@ -29,9 +29,9 @@ are not pasted into any reference.
 Nothing in this skill is legal, financial, or security advice. Regulatory, standards, and
 security material should be re-verified against primary sources at the time of use.
-## The eleven reference files
+## The twelve reference files
-The `ai-governance` skill ships eleven reference files under `references/`. The table below
+The `ai-governance` skill ships twelve reference files under `references/`. The table below
 names all eleven and maps each to its informing sources: book short names (see the
 bibliography) and the mission research notes (see below). The book short names are the
 canonical identifiers used consistently across the skill.
@@ -48,13 +48,14 @@ canonical identifiers used consistently across the skill.
 | `llm-and-agent-security.md` | Trust boundaries, prompt injection, data exposure, hallucination, excessive agency, denial of service, supply chain, red-teaming | The Developer's Playbook for LLM Security; Beyond the Algorithm; research-llm-agent-security.md |
 | `regulatory-landscape.md` | Current law by jurisdiction, compliance mapping, horizon scanning, enforcement | research-regulatory.md (authoritative); book regulatory chapters of Responsible AI in the Enterprise and Beyond the Algorithm as historical context only |
 | `procurement-third-party-and-board-oversight.md` | Vendor and model due diligence, supply chain, board reporting, metrics, audit | The AI Product Manager's Handbook; Developing Cybersecurity Programs and Policies; research-org-board-governance.md |
-| `source-index.md` | This file: provenance, attribution, bibliography | All twelve books; all five research notes (meta) |
+| `gxp-and-data-integrity.md` | GxP AI governance, ALCOA+, data integrity, electronic records, audit trails, risk-based assurance, QMS interfaces | FDA, MHRA, PIC/S, WHO, EMA, ICH, ISPE; gxp-ai-governance-brief.md; gxp-ai-governance-log.md |
 | `source-index.md` | This file: provenance, attribution, bibliography | All twelve books; all five research notes; GxP research artifacts (meta) |
 ## Research notes
-Five research notes, produced during the research foundation milestone, de-stale the books
+Seven research notes, produced during the research foundation milestone, de-stale the books
-against where AI governance stands today. They live in the mission library and are cited by
+against where AI governance stands today. They live in this skill's research directory and are cited by
-short filename throughout the skill. All five are used:
+short filename throughout the skill. All seven are used, with the two GxP artifacts informing the GxP reference:
 - research-regulatory.md — current laws and enforcement across jurisdictions (EU AI Act,
  GDPR, US federal/state, UK, China, sectoral rules), the authoritative basis for
@@ -71,6 +72,8 @@ short filename throughout the skill. All five are used:
  fiduciary duty, maturity models, third-party risk; informs `foundations-and-principles.md`,
  `governance-operating-model.md`, and `procurement-third-party-and-board-oversight.md`.
 - gxp-ai-governance-brief.md and gxp-ai-governance-log.md — GxP, ALCOA+, data integrity, electronic records, validation/assurance, and QMS interfaces; inform `gxp-and-data-integrity.md`.
 ## Bibliography — the twelve source books
 These are the twelve titles harvested for the skill, drawn from the mission's read-only
@@ -0,0 +1,102 @@
 # GxP AI Governance Research Brief
 **Research question:** What must the `ai-governance` skill add to support a job requirement for AI governance aligned with GxP, ALCOA+, and data integrity?
 **Decision context:** Determine whether the skill fully covers the job requirement and identify a bounded, reusable extension without turning the skill into legal advice or a complete pharmaceutical quality-system manual.
 **Scope:** Governance of AI systems that create, transform, analyze, or influence GxP-relevant records, decisions, processes, or submissions. Includes data integrity, electronic records, validation/assurance, lifecycle controls, quality ownership, and AI-specific evidence. Excludes legal interpretation, detailed SOP authoring, site-specific validation protocols, and implementation of production security controls.
 **Evidence standard:** Prefer current official regulator, inspectorate, and standards-body sources. Industry guidance is used for implementation context and is labeled accordingly. Regulatory claims require re-verification at use time.
 ## Executive summary
 The existing skill covers the general governance architecture needed for GxP AI: accountable ownership, risk tiering, lifecycle gates, lineage, auditability, monitoring, vendor due diligence, and regulatory mapping. It does not yet cover the regulated operating vocabulary and evidence expected for GxP systems: ALCOA+ data-integrity attributes, computerized-system validation or risk-based assurance, electronic-record and electronic-signature controls, audit-trail governance, quality-unit oversight, deviation/CAPA/change control, periodic review, and the special validation and performance evidence needed when AI contributes to regulated decisions or processes.
 The appropriate extension is a focused reference, not a claim that the skill makes a system compliant. That reference should add a GxP overlay to every AI lifecycle gate and require a documented boundary between general AI governance and the organization's QMS, CSV/CSA, data-integrity, privacy, and security owners.
 ## Key findings
 ### 1. GxP data integrity is a quality-system concern, not merely better data lineage
 FDA's drug-CGMP data-integrity guidance frames data integrity within CGMP requirements. MHRA's GxP guidance covers compliant data-governance expectations across GLP, GCP, GMP, GDP, and pharmacovigilance. PIC/S PI 041-1 and WHO guidance make ALCOA+ attributes operational expectations for records and data, including audit trails and lifecycle controls.
 **Implication for the skill:** Add an explicit ALCOA+ control review. Existing fields such as provenance, lineage, quality, and retention are necessary but do not ask whether records are attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring, and available.
 ### 2. Electronic records and audit trails need explicit controls
 21 CFR Part 11 requires controls for electronic records and signatures, including validation, record protection and retrieval, access limitation, and secure, computer-generated, time-stamped audit trails. WHO guidance calls for GxP-relevant audit trails to be enabled and periodically verified throughout the data life cycle.
 **Implication for the skill:** A GxP deployment gate must ask whether the system creates or relies on regulated electronic records, whether audit trails capture create/modify/delete events, whether they are protected and reviewed, whether signatures are attributable and non-repudiable, and whether records remain retrievable for the retention period.
 ### 3. Validation/assurance and change control are missing
 GAMP 5 Second Edition explicitly addresses risk-based compliant GxP computerized systems and adds AI/ML, cloud, open-source, and data-integrity considerations. FDA and ICH materials likewise connect computerized systems, validation, quality risk management, and data integrity.
 **Implication for the skill:** Add a risk-based validation/assurance decision at intake and build gates. For adaptive or probabilistic AI, the evidence plan must define the intended use, model/data version, acceptance criteria, performance envelope, change boundaries, revalidation triggers, and rollback or retirement path.
 ### 4. AI introduces evidence questions beyond ordinary computerized-system validation
 EMA's reflection paper on AI in the medicinal-product lifecycle emphasizes data integrity for model development and generalizability of performance to the target population and context of use. FDA's 2025 draft guidance addresses AI used to produce information or data supporting regulatory decision-making for drugs and biological products. These are guidance materials, not a universal AI validation standard.
 **Implication for the skill:** Add AI-specific evidence requirements: context-of-use statement, representative data and provenance, training/evaluation separation, performance by relevant subgroups and operating conditions, uncertainty and failure handling, human review, model/version traceability, monitoring for drift, and controls for model or data changes.
 ### 5. Governance must connect to the QMS instead of creating a parallel bureaucracy
 The sources converge on accountability, risk management, documentation, auditability, and lifecycle control, but they do not imply that an AI council replaces the quality unit or validation process.
 **Implication for the skill:** Add an ownership map that distinguishes the AI governance body from QA/quality unit, system owner, process owner, data owner, validation/assurance lead, privacy, security, regulatory, and supplier-quality roles. Exceptions, deviations, CAPA, change requests, and periodic reviews must land in the authoritative QMS or linked controlled records.
 ## Capability gap map
 | Capability | Existing skill | Required extension |
 |---|---|---|
 | AI governance operating model | Strong | Add QMS and quality-unit interfaces |
 | Risk tiering | Strong | Add GxP criticality and patient/product/data-integrity impact |
 | Lifecycle gates | Strong | Add GxP evidence and release criteria per gate |
 | Data lineage/provenance | Strong | Add ALCOA+ and complete data lifecycle review |
 | Electronic records/signatures | Minimal | Add Part 11 / equivalent control prompts, without legal interpretation |
 | Audit trails | General auditability | Add enablement, protection, review, retention, and exception handling |
 | Validation/assurance | Implied by evidence gates | Add risk-based CSV/CSA decision and validation evidence inventory |
 | AI performance evidence | General evaluation | Add context of use, generalizability, uncertainty, drift, and change triggers |
 | QMS operations | Not covered | Add deviation, CAPA, change control, periodic review, training, and SOP interfaces |
 | Supplier oversight | Strong general due diligence | Add supplier quality, model/data provenance, audit rights, change notification, and continuity |
 | Regulatory mapping | Strong generic mapping | Add GxP overlays and require primary-source verification |
 ## Recommended skill change
 Add `references/gxp-and-data-integrity.md` with:
 1. A boundary statement: educational governance guidance, not legal advice or a validation package.
 2. A GxP applicability/intake screen.
 3. ALCOA+ and data-lifecycle control prompts.
 4. Risk-based validation/assurance decision logic.
 5. GxP AI lifecycle gates and required evidence.
 6. QMS ownership and escalation interfaces.
 7. Supplier/model due diligence additions.
 8. A verification checklist and primary-source links.
 Add at least one evaluation case for a life-sciences AI system and assert that the answer distinguishes general AI governance from GxP validation and QMS responsibilities.
 ## Confidence and limitations
 - **High confidence:** The skill currently has a material GxP/ALCOA+ coverage gap. This is supported by direct comparison between the skill's current references and multiple official or standards-body sources.
 - **High confidence:** ALCOA+, audit trails, electronic-record controls, lifecycle integrity, and validation/assurance need explicit treatment.
 - **Moderate confidence:** The exact evidence package for a particular AI use case depends on GxP domain, jurisdiction, intended use, system boundary, and the organization's QMS. No universal AI validation recipe should be asserted.
 - **Open question:** Which GxP domains matter most for the target role: GMP manufacturing, GCP clinical trials, GLP laboratories, GDP distribution, or pharmacovigilance? The reference should remain cross-domain, with domain-specific obligations routed to QA/regulatory specialists.
 ## Sources
 Accessed 2026-08-20 UTC.
 1. FDA, *Data Integrity and Compliance With Drug CGMP*, https://www.fda.gov/media/119267/download
 2. FDA, *Part 11, Electronic Records; Electronic Signatures — Scope and Application*, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/part-11-electronic-records-electronic-signatures-scope-and-application
 3. eCFR, *21 CFR Part 11 — Electronic Records; Electronic Signatures*, https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11
 4. MHRA, *GxP Data Integrity Guidance and Definitions*, https://www.gov.uk/government/publications/guidance-on-gxp-data-integrity
 5. PIC/S, *PI 041-1 Guidance on Data Integrity*, https://picscheme.org/docview/4234
 6. WHO, *TRS 1033 Annex 4: Guideline on Data Integrity*, https://www.who.int/docs/default-source/medicines/norms-and-standards/guidelines/inspections/trs1033-annex4-guideline-on-data-integrity.pdf
 7. EMA, *Guideline on Computerised Systems and Electronic Data in Clinical Trials*, https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-and-electronic-data-clinical-trials_en.pdf
 8. EMA, *Reflection Paper on the Use of Artificial Intelligence in the Medicinal Product Lifecycle*, https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf
 9. FDA, *Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products*, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological
 10. ICH, *Q9(R1) Quality Risk Management*, https://database.ich.org/sites/default/files/ICH_Q9(R1)_Guideline_Step4_2022_1219.pdf
 11. ISPE, *GAMP 5 Guide, 2nd Edition*, https://ispe.org/publications/guidance-documents/gamp-5-guide-2nd-edition
 12. European Commission, *EudraLex Volume 4, Annex 11: Computerised Systems*, https://health.ec.europa.eu/system/files/2016-11/annex11_01-2011_en_0.pdf
@@ -0,0 +1,91 @@
 # Research Log: GxP AI Governance Gap
 **Question:** What must `ai-governance` add to support GxP, ALCOA+, and data-integrity expectations?
 **Track:** Academic/comprehensive, narrow technical/regulatory investigation
 **Started/completed:** 2026-08-20 UTC
 **Inclusion criteria:** Official regulators, inspectorates, standards bodies, and established life-sciences guidance directly addressing GxP data integrity, electronic records, computerized systems, validation/assurance, or AI in the medicinal-product lifecycle.
 **Exclusion criteria:** Vendor marketing, generic responsible-AI commentary, legal interpretation, and sources that only mention AI without GxP/data-integrity relevance.
 ## Search record
 | Query | Purpose | Result |
 |---|---|---|
 | `site:fda.gov data integrity ALCOA+ guidance pharmaceutical CGMP official` | FDA data-integrity baseline | Retained FDA CGMP data-integrity guidance |
 | `site:fda.gov 21 CFR Part 11 electronic records electronic signatures official guidance` | Electronic-record controls | Retained FDA Part 11 guidance and eCFR |
 | `site:ema.europa.eu Annex 11 computerized systems GMP official data integrity` | EU computerized-system expectations | Retained EMA computerized-system and Annex 11 materials |
 | `site:gov.uk MHRA GxP data integrity guidance ALCOA official` | Cross-GxP inspectorate guidance | Retained MHRA final guidance |
 | `site:ich.org ICH Q9(R1) quality risk management computerized systems data integrity official` | Risk-management baseline | Retained ICH Q9(R1) |
 | `site:fda.gov artificial intelligence machine learning drug manufacturing quality systems guidance` | AI-specific FDA context | Retained FDA AI drug-development/manufacturing materials |
 | `site:ema.europa.eu artificial intelligence reflection paper medicines lifecycle governance data integrity` | AI-specific EMA context | Retained EMA AI reflection paper |
 | `site:picscheme.org PI 041 data integrity ALCOA official PDF` | ALCOA+ operational detail | Retained PIC/S PI 041-1 |
 | `site:who.int guidance data integrity ALCOA pharmaceutical official` | Independent global corroboration | Retained WHO data-integrity guidance |
 | `site:ispe.org GAMP 5 second edition artificial intelligence machine learning regulated systems` | Industry implementation context | Retained ISPE GAMP 5 description |
 ## Source decisions and extracted claims
 ### Retained: FDA CGMP data integrity guidance
 - **Authority:** Tier 1 official regulator.
 - **Relevant claim:** Data integrity is part of CGMP compliance for drugs under 21 CFR parts 210, 211, and 212.
 - **Use in synthesis:** Establishes that data integrity belongs inside the quality and compliance system, not only in an AI/data platform.
 - **Limitation:** Drug-CGMP focus; does not by itself cover every GxP domain or AI-specific validation.
 ### Retained: FDA Part 11 guidance and eCFR Part 11
 - **Authority:** Tier 1 regulator and codified regulation.
 - **Relevant claims:** Electronic records/signatures require controls for authenticity, integrity, confidentiality where appropriate, record retrieval, access control, validation, and secure time-stamped audit trails.
 - **Use in synthesis:** Justifies an explicit electronic-record/audit-trail gate.
 - **Limitation:** Applicability depends on the system's records and regulated use; this research does not make a legal applicability determination.
 ### Retained: MHRA GxP data-integrity guidance
 - **Authority:** Tier 1 inspectorate guidance.
 - **Relevant claim:** Guidance applies to data-integrity expectations across GxP sectors including GLP, GCP, GMP, GDP, and pharmacovigilance.
 - **Use in synthesis:** Supports a cross-GxP reference and a data-governance/QMS boundary.
 - **Limitation:** Guidance is not a substitute for jurisdiction-specific legal or quality advice.
 ### Retained: PIC/S PI 041-1 and WHO data-integrity guidance
 - **Authority:** Tier 1 international inspection/health authority guidance.
 - **Relevant claims:** ALCOA+ attributes and audit-trail lifecycle controls make data usable for informed decisions and support integrity across the data life cycle.
 - **Use in synthesis:** Provides the operational vocabulary missing from the current skill.
 - **Limitation:** These are guidance documents; exact adoption and terminology can vary by authority and domain.
 ### Retained: EMA computerized-system and AI materials
 - **Authority:** Tier 1 regulator.
 - **Relevant claims:** Computerized-system data security includes integrity, reliability, and availability; AI lifecycle considerations include integrity of model-development data and generalizability to the target population and context of use.
 - **Use in synthesis:** Supports adding AI-specific performance/context evidence to GxP lifecycle gates.
 - **Limitation:** The AI reflection paper is guidance/reflection material, not a universal validation standard.
 ### Retained: European Commission EudraLex Volume 4 Annex 11
 - **Authority:** Tier 1 European Commission good-manufacturing-practice guidance.
 - **Relevant claim:** Annex 11 provides the computerized-system control context relevant to regulated records, system operation, validation, and data integrity.
 - **Use in synthesis:** Supports the explicit electronic-record, audit-trail, validation/assurance, and QMS-interface prompts in the GxP overlay.
 - **Limitation:** Applicability and current interpretation depend on the system boundary, GxP domain, jurisdiction, and responsible quality/regulatory functions.
 ### Retained: ICH Q9(R1)
 - **Authority:** Tier 1 international harmonization guidance.
 - **Relevant claim:** Quality risk management provides the framework for risk-based decisions, including computerized-system and data-integrity concerns.
 - **Use in synthesis:** Supports risk-based validation/assurance rather than one fixed control burden.
 - **Limitation:** It is a framework, not an implementation procedure for a specific AI system.
 ### Retained: ISPE GAMP 5, 2nd Edition
 - **Authority:** Tier 2 established industry guidance.
 - **Relevant claim:** The second edition addresses risk-based compliant GxP computerized systems and includes AI/ML, cloud, open-source, and data-integrity topics.
 - **Use in synthesis:** Supplies implementation vocabulary for validation/assurance and supplier/system lifecycle controls.
 - **Limitation:** Industry guidance, not law or regulator-issued binding requirements.
 ## Rejected or not promoted
 - Generic AI governance and responsible-AI sources: redundant with the existing skill and not specific enough to fill this gap.
 - Vendor blogs and certification marketing: excluded because the question requires an authoritative baseline and vendor incentives would add little evidence.
 - Search snippets without a retrievable primary document: used only for discovery, not as standalone evidence.
 ## Synthesis status
 The research reached saturation for the bounded question: every retained source adds one of the same convergent control families — data integrity attributes, electronic records/audit trails, risk-based assurance, lifecycle traceability, AI context/performance evidence, or QMS accountability. The remaining uncertainty is domain- and jurisdiction-specific applicability, which the new reference must explicitly route to QA/regulatory owners rather than resolve itself.
@@ -9,7 +9,7 @@
 - [agent-evals-and-observability](agent-evals-and-observability/SKILL.md): Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, regression analysis, release gates, production traces, or privacy-aware telemetry. Covers task and trajectory contracts, statistical comparisons, and incident-to-case learning; route framework implementation to pydanticai or langgraph when needed.
 - [agent-production-operations](agent-production-operations/SKILL.md): Operate an evaluated agent with tools and authority in production through a runtime control plane covering versioning, staged rollout, fallback, cost and latency budgets, tool health, human escalation, disablement, and trace-to-eval feedback. Do not use for building agents, designing evals, or general release, SRE, security, or platform engineering — those methods stay with their specialist skills.
 - [agent-skills](agent-skills/SKILL.md): Use this skill when creating, reviewing, or editing Agent Skills-format skills, or when implementing skill discovery and loading in an agent client. It covers directory structure, SKILL.md metadata, progressive disclosure, evals, and repository conventions. Do not use this skill for general software work that does not involve the Agent Skills format or lifecycle.
- [ai-governance](ai-governance/SKILL.md): Design and operate an organization's AI governance system: define governance principles, operating models and decision rights, risk frameworks, lifecycle gates, and fairness, transparency, privacy, security, regulatory, and board-oversight controls. Use when standing up a governance program, tiering AI use-case risk, reviewing an LLM or agent system for governance and safety gaps, mapping a regulation to a compliance plan, scoring governance maturity, or preparing board reporting. Do not use for interpreting regulations as legal advice (route to legal-strategy), data-governance mechanics (data-architect/data-engineering), or implementing application security (secure-software-engineering).
+- [ai-governance](ai-governance/SKILL.md): Design and operate an organization's AI governance system: define governance principles, operating models and decision rights, risk frameworks, lifecycle gates, and fairness, transparency, privacy, security, regulatory, and board-oversight controls. Use when standing up a governance program, tiering AI use-case risk, reviewing an LLM or agent system for governance and safety gaps, mapping a regulation to a compliance plan, scoring governance maturity, or preparing board reporting. For regulated life-sciences use cases, also cover GxP, ALCOA+, data integrity, electronic records, validation/assurance, and QMS interfaces. Do not use for interpreting regulations as legal advice (route to legal-strategy), data-governance mechanics (data-architect/data-engineering), or implementing application security (secure-software-engineering).
 - [anydoc](anydoc/SKILL.md): Convert Word (.doc/.docx/.docm), PowerPoint (.ppt/.pps/.pot/.pptx/.pptm/.ppsx/.ppsm), Excel (.xls/.xlsx/.xlsm/.xlsb), OpenDocument (.odt/.ods/.odp), RTF, EPUB, CSV, and PDF documents to clean GitHub-Flavored Markdown locally with the Any Doc CLI (npx -y @firecrawl/anydoc@0.1.6): headings, GFM tables, slide structure, and footnotes in one pass. Use when a task needs the contents of an office document, spreadsheet, presentation, ebook, or PDF you cannot read directly. Do not use for generating, editing, or validating documents (use documents), for ebook packaging (use epub), or for OCR of scanned or image-only PDFs (anydoc does not OCR; route to OCR tooling).
 - [api-design-and-evolution](api-design-and-evolution/SKILL.md): Design, document, review, and evolve consumer-facing APIs and event interfaces. Use when choosing REST/HTTP, GraphQL, RPC, events, webhooks, or streaming; writing OpenAPI or AsyncAPI contracts; defining schemas, pagination, mutations, errors, idempotency, or API compatibility; or planning API versioning, deprecation, and migration. Use secure-software-engineering for a full security lifecycle, ADR authoring for durable architecture decisions, and spec-driven-development for a delivery specification and implementation gates.
 - [artifact-pyramids](artifact-pyramids/SKILL.md): Organize durable agent research outputs as summaries, analysis, and evidence dossiers. Use when producing multi-layer research artifacts or coordinating research handoffs.
@@ -23,7 +23,7 @@ Each skill's `description` field is the canonical routing contract. This conveni
 | "traefik", "reverse proxy", "load balancer", "API gateway", "Let's Encrypt", "ACME", "Docker routing", "traefik.yml", "entry point", "middleware", "TLS termination", "forward auth", "rate limit" | [traefik](../traefik/SKILL.md) |
 | "reverse-engineer", "understand this codebase", "PRD from code", "architecture document" | [software-architecture-analysis](../software-architecture-analysis/SKILL.md) |
 | "data architecture", "data platform", "data strategy", "data mesh", "governance" | [data-architect](../data-architect/SKILL.md) |
-| "AI governance", "AI governance program", "responsible AI", "model risk", "AI risk tier", "AI risk tiering", "use-case risk tier", "governance operating model", "AI decision rights", "AI governance council", "AI risk framework", "NIST AI RMF", "ISO 42001", "AI lifecycle gate", "AI fairness review", "AI transparency", "explainability review", "LLM governance review", "agent safety review", "prompt injection governance", "AI regulatory compliance", "EU AI Act", "AI compliance plan", "governance maturity assessment", "AI board reporting", "board AI governance", "third-party AI due diligence", "model card" | [ai-governance](../ai-governance/SKILL.md) |
+| "AI governance", "AI governance program", "responsible AI", "model risk", "AI risk tier", "AI risk tiering", "use-case risk tier", "governance operating model", "AI decision rights", "AI governance council", "AI risk framework", "NIST AI RMF", "ISO 42001", "AI lifecycle gate", "AI fairness review", "AI transparency", "explainability review", "LLM governance review", "agent safety review", "prompt injection governance", "AI regulatory compliance", "EU AI Act", "AI compliance plan", "GxP AI governance", "ALCOA+", "data integrity governance", "computerized system validation", "AI in regulated life sciences", "AI QMS interface", "governance maturity assessment", "AI board reporting", "board AI governance", "third-party AI due diligence", "model card" | [ai-governance](../ai-governance/SKILL.md) |
 | "de-spin", "is this spin", "propaganda", "misdirection", "true lies", "what is this leaving out", "misleading claim", "selective framing", "evidence theater", "separate what is true false complicated" | [de-spin](../de-spin/SKILL.md) |
 | "digital twin", "digital twins", "digital twin universe", "digital thread", "software process twin", "DevOps twin", "cyber-cyber twin", "twin architecture", "twin synchronization", "twin fidelity", "twin health", "twin drift", "predictive maintenance twin", "virtual commissioning", "agentic software factory", "dark factory" | [digital-twin](../digital-twin/SKILL.md) |
 | "daily check-in", "day debrief", "conversational journaling", "grill me", "interview me about my life", "learn how I work", "what could an AI help me with", "personal AI boundaries", "proactive check-in", "daily life" | [daily-life-discovery](../daily-life-discovery/SKILL.md) |