precision-agriculture

Frontend-engineering team — 4 agents (frontend-architect, react-implementation-engineer, frontend-state-and-data-engineer, frontend-performance-engineer) for building app-grade frontends (React/Next, TypeScript): component architecture and composition, the rendering-strategy choice (CSR/SSR/SSG/ISR/React Server Components), client state vs server-cache (TanStack Query/RSC) and form/validation, bundle/performance discipline (code-splitting, Core Web Vitals, hydration cost), and accessibility-in-code (semantic HTML, ARIA, keyboard, focus). TypeScript-first. 5 skills, a decision-tree knowledge bank (rendering-strategy + state-location trees + a dated 2026 map), 12 best-practices, 4 templates, 4 commands, 1 advisory hook. Distinct from web-design (brand/UX/marketing sites). Seams: design/brand/WCAG-audit -> web-design, auth UX -> auth-identity, API consumption -> api-engineering, native -> mobile-engineering. Requires ravenclaude-core@>=0.7.0.

Corporate legal-operations & contract-lifecycle-management (CLM) team — 3 agents (legal-ops-lead, contract-review-specialist, obligations-and-renewals-analyst) for the operational layer of an in-house legal/legal-ops function: legal intake & triage and the request workflow, contract playbooks & matter management, clause libraries & redline review with fallback/standard positions and key-term extraction, and obligation/renewal/auto-renew tracking with a contract repository, metadata model, reporting & alerts. This is operational/process support, NOT legal advice — a lawyer signs off on legal judgement. Decision-tree knowledge bank (5 Mermaid trees + dated CLM map), 12 best-practices, 3 skills, 3 commands, 2 templates, 1 advisory hook, 5 scenarios, a stdlib renewal-window/cycle-time/obligation-aging calc. Seams: a law firm's own business -> legal-small-firm; supplier contracts -> procurement-sourcing; DPA/privacy clauses -> data-governance-privacy. Requires ravenclaude-core@>=0.7.0.

Exploratory data science & reproducible research team — 3 agents (exploratory-data-scientist, feature-and-modeling-engineer, research-reproducibility-engineer) for the analysis layer between raw data and a defensible result: data profiling/cleaning, EDA, hypothesis generation, communicating findings with uncertainty; feature engineering, classical modeling (regression, trees, boosting), model selection, evaluation (cross-validation, metrics) and leakage avoidance; and the reproducibility spine — notebook hygiene, pinned environments, experiment tracking, data/version control, seeds, and reproducible pipelines. 12 best-practices, decision-tree knowledge bank, 3 skills, 3 commands, 2 templates, 1 hook, scenarios bank, and stdlib-only ds_calc.py (classification/regression metrics, split-check). Seams: is-the-result-statistically-real -> applied-statistics; productionize/serve/monitor -> ml-engineering; data pipelines/warehouse -> data-platform. Requires ravenclaude-core@>=0.7.0.

Backend-engineering team — 4 agents (backend-architect, service-implementation-engineer, backend-data-access-engineer, backend-reliability-engineer) for the application/service layer behind an API: domain modeling and service boundaries (monolith vs services, where to split), business-logic implementation and error handling, the data-access layer (repository pattern, ORM use, transaction boundaries, killing N+1), caching strategy (cache-aside, invalidation, stampede), background jobs and async messaging (idempotent workers, outbox, DLQs), and backend reliability (timeouts, retries with backoff + jitter, circuit breakers, graceful degradation). Language-agnostic (examples in Node/Python/Go). 6 skills, a decision-tree knowledge bank (service-boundary + cache trees + a dated map), 12 best-practices, 4 templates, 4 commands, 1 advisory hook. Seams: the contract -> api-engineering, the schema/index -> database-engineering, deploy -> devops-cicd. Requires ravenclaude-core@>=0.7.0.

Automotive Dealership Operations specialist team — 4 agents (dealership-operations-lead, sales-desking-analyst, fixed-ops-service-specialist, fi-products-specialist), 5 skills, 4 templates, 5 commands, 1 advisory hook, 8 best-practice rules, and a 4-file research-grounded knowledge bank. An operations team for a dealer principal or GM accountable for total gross, fixed-ops absorption, and inventory turn — it runs the store on fixed-ops not new-car gross, manages days-supply and floorplan as carrying-cost cash, reads total gross per unit as front plus back, and treats absorption as the survival metric. Inherits ravenclaude-core protocols.

Clinical Trials specialist team — 4 agents, 5 skills, 4-file cited knowledge bank, 3 templates, 5 commands, 8 best-practice rules, 1 advisory hook. A clinical-operations team for a sponsor, CRO, or site network — it designs feasible protocols (because eligibility criteria drive the enrollment failure that hits two-thirds of sites), plans patient recruitment against a ~$6,533 per-patient cost (and ~$19,533 to replace), manages site activation and the ~30% dropout, and frames the regulatory submission the way a study where 80% run late demands. Inherits ravenclaude-core protocols.

QA & test-automation team — 3 agents (test-strategy-architect, e2e-automation-engineer, test-infrastructure-engineer) for a test suite that catches real defects without becoming a tax: the test pyramid (many fast unit, fewer integration, few E2E), deterministic end-to-end automation (Playwright/Cypress, resilient selectors, no fixed sleeps), test data and ephemeral environments, meaningful coverage (mutation over line %), and flaky-test quarantine. Deepens ravenclaude-core/tester-qa. 5 skills, a decision-tree knowledge bank (test-level + flake-triage trees + a dated 2026 tooling map), 12 best-practices, 4 templates, 4 commands, 1 advisory hook. Seams: contract tests -> api-engineering, CI gating -> devops-cicd, UI under test -> frontend-engineering. Requires ravenclaude-core@>=0.7.0.

Veterinary Practice specialist team — 4 agents, 5 skills, 4-file cited knowledge bank, 3 templates, 5 commands, 8 best-practice rules, 1 advisory hook. A clinical-and-practice-management team for a veterinary hospital owner or medical director — it builds standardized care protocols, runs the practice on production and ACT (average client transaction), manages the appointment-and-doctor capacity that gates revenue, and frames the independent-vs-corporate position in a fast-consolidating market. Inherits ravenclaude-core protocols.

Game Development specialist team — 4 agents, 5 skills, 4-file cited knowledge bank, 3 templates, 5 commands, 8 best-practice rules, 1 advisory hook. A production-and-design team for a game studio or indie team — it scopes to a vertical slice before a full build, designs core loops and economies that retain, runs production on milestones and risk burn-down, and reads live-ops on retention and monetization the way a team that ships and then operates a game must. Inherits ravenclaude-core protocols.

Independent auto-repair shop operations team — 3 agents (auto-repair-shop-lead, service-advisor-estimator, technician-workflow-manager) for a repair shop's three engines: shop P&L economics (effective labor rate, tech productivity-efficiency-proficiency, labor + parts gross profit, comeback rate, car count); the service-advisor front counter (write-up, digital vehicle inspection, inspection-to-estimate, approval, declined-work follow-up, ethical upsell); and technician workflow (dispatch, flat-rate vs actual hours, WIP/RO aging, parts staging, comeback control). Ships 4 skills, a decision-tree knowledge bank (4 Mermaid trees + a dated 2026 reference), 5 best-practices, 2 templates, 2 commands. Operations/financial decision-support, not legal/tax/OEM-warranty advice; labor-rate norms, productivity benchmarks, and parts-matrix figures carry a retrieval date + verify-at-use; no customer PII. Seams to automotive-dealership, fleet-logistics, and skilled-trades-contracting. Requires ravenclaude-core@>=0.7.0.