From phronesis
Request, read, and verify cross-vertical forecasts from the Phronesis platform — decisions that live at the intersection of two verticals (e.g. AI/AGI x Regulatory, Energy x Compute, Climate x Supply-Chain). Use this skill when a decision depends on how two domains interact rather than on either alone.
How this skill is triggered — by the user, by Claude, or both
Slash command
/phronesis:cross_productThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Many real decisions live at the intersection of two Phronesis verticals: an
Many real decisions live at the intersection of two Phronesis verticals: an AI/AGI capability trajectory conditioned on regulatory cadence, datacenter build-out conditioned on grid capacity, or critical-mineral supply conditioned on climate risk. Phronesis V#X x V#Y cross-product forecasting models that interaction directly. Use this skill when a decision depends on how two domains move together — not on either domain in isolation.
https://phronesis-jrstinehour.replit.appV1 Energy, V2 Compute + AI Infrastructure, V3 Healthcare, V4 Climate,
V5 Regulatory, V6 Supply-Chain, V7 Space, V8 Robotics, V9 AI/AGI & Compute,
V10 Physics & Materials Discovery, V11 Longevity & Human Health, V12 Quantum
Computing. Per-vertical archetypes and live status are listed at GET /v1/catalog.
Name both verticals; pick the archetype from whichever vertical is the primary forecast target, and pass the second vertical as the conditioning context:
POST /v1/decision/forecast
Authorization: Bearer <JWT>
Content-Type: application/json
{
"vertical": "V9",
"cross_vertical": "V5",
"archetype": "agi-timeline-forecast",
"subject": "ai_agi",
"question": "Probability a frontier model meets the AGI capability bar before 2030, conditioned on the pace of AI-safety regulation",
"horizon": "2030",
"compute_tier": "strategic"
}
Cross-product questions are inherently higher-variance; strategic or
strategic-sync compute tiers are usually warranted.
Contract-v1 envelope { "status": "ok", "data": {...}, "request_id": "..." }. The
data decision forecast contains a point forecast (p50), a monotonic
uncertainty band (p10 / p50 / p90), enumerated assumptions — for a
cross-product forecast the assumptions name the linkage between the two verticals —
the sources citation chain, sensitivity drivers referencing assumption ids,
the cost attestation, and an audit_trail_id.
When either vertical in the crossing is governed (e.g. V3 / V11 population-aggregate discipline, V12 US-and-allied vendor-citation rule), that vertical's governance still applies to the cross-product forecast.
GET /v1/trust/receipt/{forecast_id} — public signed Trust Receipt.GET /calibration/leaderboard — check the accuracy rows for both verticals.GET /v1/substrate/completeness — substrate-completeness for both verticals.question — what depends on
what — so the forecast models the right direction of influence.audit_trail_id so the cross-domain decision stays auditable.npx claudepluginhub sustainable-finance-partners/skills --plugin phronesisProvides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Fetches up-to-date documentation from Context7 for libraries and frameworks like React, Next.js, Prisma. Use for setup questions, API references, and code examples.