Risk & Compliance (GRC) Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
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See how to installReview the instructions the agent will receive before you install.
Risk & Compliance (GRC) Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
28 native personas (12 cross-listed) across 7 cells. Real public figures
rendered as illustrative composites — see superintelligence/DISCLAIMER.md. Built local-first +
Claude-research, validator-gated (real cited URLs, no fabrication).
| Cell | Personas | Focus |
|---|---|---|
privacy-data-protection | 6 | Privacy law, data protection, GDPR/CCPA, privacy-by-design. |
ai-governance-responsible-ai | 8 | AI governance, responsible AI, algorithmic accountability, EU AI Act / NIST RMF. |
regulatory-tech-policy | 6 | Tech regulation, antitrust, platform policy, algorithmic-accountability journalism/law. |
security-governance | 5 | Security governance, disclosure, threat policy (cross-listed from Engineering). |
erm-grc-leadership | 6 | Enterprise risk management, GRC frameworks, internal audit, three-lines model. |
financial-crime-aml | 4 | AML/KYC, sanctions, financial-crime compliance. |
model-systemic-risk | 5 | Model risk, validation (SR 11-7), systemic/financial-stability risk (cross-listed from Trading). |
/SI-GRC-Orchestrate "<prompt>" → picks 16-32 personas + approval gate/SI-GRC-Decide · -Tradeoff · -Pre-Mortem · -Review · -Stress-Test · -Plan · -Design · -Analyse · -Vote · -Debug · -Defend · -Roast · -Post-Mortem · -Re-Analyse · -Full-Cycle/SI-GRC-Ask <slug> "<q>" · -Huddle <cell> "<topic>" · -Meeting "<prompt>" · -Read <slug>/SI-GRC-Recruit · -Refresh · -VoiceCheck · -VerifyFor multi-domain decisions, the top-level /SI-Orchestrate (+ /SI-<Verb>) routes ACROSS teams
via superintelligence/scripts/meta_select.py (local nomic-embed). This team auto-joins that panel.
registry.json (generated from persona frontmatter) + cells/*.md + roster.json.
Regenerate: python3 risk-compliance/scripts/build_registry.py && python3 risk-compliance/scripts/build_cells.py.
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Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.
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Post-run self-evaluation system that scores agent output on correctness, clarity, actionability, and conciseness. Use after /team runs, skill executions, or when explicitly asked to evaluate output quality.
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Create AI marketing videos for ads, promos, product launches, and brand content. Models: Veo, Seedance, Wan, FLUX for visuals, Kokoro for voiceover. Types: product demos, testimonials, explainers, social ads, brand videos. Use for: Facebook ads, YouTube ads, product launches, brand awareness. Triggers: marketing video, ad video, promo video, commercial, brand video, product video, explainer video, ad creative, video ad, facebook ad video, youtube ad, instagram ad, tiktok ad, promotional video, launch video
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Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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Your AI research and engineering brain trust. 59 named personas across 8 cells covering frontier labs, applied product, model architecture, reasoning/RL/agents, alignment and interpretability, theory and science of DL, multimodal and…
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Use when designing a new REST or GraphQL API, reviewing an API spec before implementation, setting team API standards, or migrating REST to GraphQL. Covers resources, HTTP semantics, pagination, error handling, and pitfalls.
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