Your climate and energy brain trust. 25 named personas across 7 cells covering energy transition, carbon markets, climate modeling, sustainability, grid infrastructure, environmental policy, and ESG.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
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Your climate and energy brain trust. 25 named personas across 7 cells covering energy transition, carbon markets, climate modeling, sustainability, grid infrastructure, environmental policy, and ESG.
| Cell | Count | Focus |
|---|---|---|
carbon-markets-climate-finance | 3 | Carbon Markets Climate Finance |
climate-modeling-science | 4 | Climate Modeling Science |
energy-transition-renewables | 5 | Energy Transition Renewables |
environmental-policy-regulation | 3 | Environmental Policy Regulation |
esg-corporate-sustainability | 3 | Esg Corporate Sustainability |
grid-infrastructure-storage | 4 | Grid Infrastructure Storage |
sustainability-tech-circularity | 3 | Sustainability Tech Circularity |
Listed by cell. Each has a YAML-frontmatter profile under personas/<slug>.md.
Carbon Markets Climate Finance (3): charlotte-streck · christiana-figueres · henry-derwent
Climate Modeling Science (4): gavin-schmidt · joeri-rogelj · katharine-hayhoe · michael-mann
Energy Transition Renewables (5): amory-lovins · fatih-birol · kelly-gallagher · michael-liebreigh · tony-seba
Environmental Policy Regulation (3): gina-mccarthy · johan-rockstrom · mary-robinson
Esg Corporate Sustainability (3): george-serafeim · robert-eccles · tensie-whelan
Grid Infrastructure Storage (4): jesse-jenkins · jigar-shah · mateo-jaramillo · ramez-naam
Sustainability Tech Circularity (3): ellen-macarthur · janine-benyus · william-mcdonough
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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…
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.