justin-sun-perspective
Provides a Justin Sun-inspired perspective for analyzing crypto industry trends, business decisions, competitive strategy, regulation, attention economics, and entrepreneurial cho…
Provides a methodology for designing and operating AI governance programs, including risk tiering, lifecycle controls, accountability, compliance mapping, agent reviews, GxP overlays, and board reporting.
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 across SaaS, API, self-hosted, and agentic deployment postures. 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).
evals/evals.json
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pytest.ini
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README.md
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research/gxp-ai-governance-brief.md
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research/gxp-ai-governance-log.md
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templates/agentic-governance-review.md
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templates/board-ai-governance-report.md
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templates/governance-charter.md
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templates/model-card.md
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templates/model-risk-assessment.md
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templates/third-party-due-diligence.md
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templates/use-case-intake-form.md
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references/ai-lifecycle-governance.md
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references/fairness-bias-accountability.md
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references/foundations-and-principles.md
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references/governance-operating-model.md
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references/gxp-and-data-integrity.md
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references/llm-and-agent-security.md
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references/privacy-and-data-governance.md
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references/procurement-third-party-and-board-oversight.md
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references/regulatory-landscape.md
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references/risk-management-and-frameworks.md
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references/six-level-governance-framework.md
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references/source-index.md
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references/transparency-and-explainability.md
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scripts/governance-maturity.py
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scripts/test_governance_maturity.py
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scripts/test_use_case_risk_tier.py
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scripts/use-case-risk-tier.py
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SKILL.md
SKILL.md
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Provides a Justin Sun-inspired perspective for analyzing crypto industry trends, business decisions, competitive strategy, regulation, attention economics, and entrepreneurial cho…
Helps agents design and run product governance through decision rights, review cadences, evidence standards, operating patterns, exception records, and escalation paths.
Helps evaluate whether AI-enabled workflows should be adopted, scaled, constrained, redesigned, or retired using outcome evidence, countermetrics, full operating costs, uncertaint…
Helps agents compare intended and observed product outcomes, update evidence-backed assumptions, assess feature health, choose lifecycle actions, and plan retirement with migratio…
Guides end-to-end product experiments by mapping assumptions, selecting appropriate test methods, defining guardrails and decision rules, and recording readouts that update produc…
Helps agents create and review outcome-based product roadmaps and portfolio views using strategic bets, Now/Next/Later sequencing, dependencies, scenarios, confidence, and review …
Helps agents manage technical projects through initiation, planning, delivery, recovery, handoffs, and closure using predictive, iterative, Scrum, flow-based, or hybrid approaches.
Guides agents through stakeholder mapping, interviews, gap detection, conflict resolution, synthesis, validation, and conversion of discovery findings into structured specificatio…