Data & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Data & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.
25 native personas (0 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 |
|---|---|---|
data-engineering-architecture | 4 | Pipelines, warehouses, lakehouse, data mesh, modeling, orchestration. |
analytics-engineering-modern-stack | 3 | dbt-era analytics engineering, the modern data stack, metrics layers. |
mlops-ml-systems | 4 | Productionizing ML, ML system design, applied-ML engineering, eval. |
data-science-statistics | 4 | Statistical practice, data-science tooling, decision intelligence. |
data-visualization | 4 | Visualization theory, dataviz craft, communicating with data. |
data-governance-quality | 4 | Data quality, observability, governance, contracts, leadership. |
experimentation-causal-inference | 2 | A/B testing, controlled experiments, causal inference. |
/SI-Data-Orchestrate "<prompt>" → picks 16-32 personas + approval gate/SI-Data-Decide · -Tradeoff · -Pre-Mortem · -Review · -Stress-Test · -Plan · -Design · -Analyse · -Vote · -Debug · -Defend · -Roast · -Post-Mortem · -Re-Analyse · -Full-Cycle/SI-Data-Ask <slug> "<q>" · -Huddle <cell> "<topic>" · -Meeting "<prompt>" · -Read <slug>/SI-Data-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 data-analytics/scripts/build_registry.py && python3 data-analytics/scripts/build_cells.py.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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…
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。