This orchestrator does two jobs.
---name: "product-skills"description: "Use when a product request needs routing across the product-team skills (prioritization, OKRs, UX research, experiments, discovery, roadmaps), or to run the continuous discovery loop. Routes with product_goal_router.py and gates with discovery_cadence_tracker.py and ost_linter.py."context: forklicense: MITmetadata: version: 2.11.1 author: Alireza Rezvani tags: [product, product-management, orchestrator, discovery, ux, analytics, agent-harness] compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]--- # Product Team — Domain Orchestrator & Discovery Loop This orchestrator does two jobs. **Routing:** fork context, classify a product inquirywith `scripts/product_goal_router.py` across all 16 product-team lanes (12 bundled + 4standalone plugins), run exactly one, return a digest. **Looping:** run product work asbounded agentic loops with machine-checkable gates — the continuous-discovery loop(weekly cadence scored by `discovery_cadence_tracker.py`, tree structure enforced by`ost_linter.py`) and goal-scale runs through the repo-wide agent-harness. ## When to invoke | Symptom | Sub-skill ||---|---|| "Prioritize features / RICE / PRD" | `product-manager-toolkit` || "OKRs, strategy cascade" | `product-strategist` || "Personas, usability, research synthesis" | `ux-researcher-designer` || "Design tokens, WCAG contrast" | `ui-design-system` || "Competitor matrix, teardown" | `competitive-teardown` || "Retention, cohorts, funnels, KPIs" | `product-analytics` || "A/B test, sample size, hypothesis" | `experiment-designer` || "Discovery, assumptions, opportunity trees" | `product-discovery` || "Roadmap comms, release notes, changelog" | `roadmap-communicator` || "Spec → runnable repo" | `spec-to-repo` || "Landing page (Next.js/Tailwind)" | `landing-page-generator` || "SaaS boilerplate" | `saas-scaffolder` || "User stories, sprint capacity" | `agile-product-owner` (standalone) || "Apple HIG audit" | `apple-hig-expert` (standalone) || "PRD from an existing codebase" | `code-to-prd` (standalone) || "Summarize papers/articles" | `research-summarizer` (standalone) | ## Routing logic (deterministic) ```bashpython3 scripts/product_goal_router.py --text "<the goal>" --output json``` Exit 0 → `route_to` names the skill (with `skill_path`, including the standaloneplugins): load its SKILL.md and follow its workflow. Exit 2 → ask ONE clarifying questionnaming the listed candidates, with a recommended answer. Exit 3 → no signal: ask the userto restate the goal with the deliverable named. Never guess silently; never silentlychain — digest first, confirm, then chain. ## The discovery loop (the domain's recurring agentic loop) Modern discovery is a weekly habit, not a project phase (Torres). Run it as a boundedloop with two machine gates: 1. **Observe** — maintain `discovery_log.json` (interviews, assumption tests; shape in `assets/sample_discovery_log.json`) and score the cadence: ```bash python3 scripts/discovery_cadence_tracker.py --input discovery_log.json ``` Refuses on < 2 interviews (exit 5) — there is no cadence to measure yet. Output: health 0–100, verdict HEALTHY/AT-RISK/DORMANT, named gaps, and `next_loop_action`.2. **Choose** — the tracker's `next_loop_action` IS the choice: book the touchpoint, re-anchor the guide on the outcome, or test the top untested assumption (route to `product-discovery`'s assumption_mapper for prioritization).3. **Act** — run the interview / assumption test with the routed sub-skill's tools.4. **Verify** — keep the tree structurally sound before it may drive a roadmap: ```bash python3 scripts/ost_linter.py --input ost.json # exit 2 = NEEDS-REWORK, fix before citing the tree ``` Rules: one measurable outcome root (O1), opportunities are needs not features (O2), targeted opportunities compare ≥ 2 solutions (O3), every solution has an assumption test (O4), no orphan solutions (O5 — the feature-factory tell).5. **Record / Repeat-or-stop** — update the log, keep the weekly streak alive. Stop states: HEALTHY + validated assumption → graduate to `experiment-designer` (build the A/B gate) or `product-manager-toolkit` (PRD); DORMANT for 4+ weeks → escalate to the product lead by name — do not quietly let discovery die. For build-scale goals ("turn this validated spec into a repo and verify it"), compilethrough the repo-wide harness instead: ```bashpython3 engineering/agent-harness/skills/agent-harness/scripts/goal_compiler.py \ --goal "<goal>" --manifest engineering/agent-harness/skills/agent-harness/assets/harnesses/product-team.json \ --out .agent-harness/plan.json``` The domain's three strongest close-out gates plug in as task verifications:`../spec-to-repo/scripts/validate_project.py` (exit 0), `code-to-prd`'s golden`expected_outputs/`, and `research-summarizer`'s citation-count check. ## Hard rules 1. **Evidence before conviction**: no roadmap item cites the OST unless `ost_linter.py` exits 0; no insight is asserted from a single participant (anecdote, not insight).2. **Outcome-first**: every loop hangs from one measurable outcome — the linter's O1 rule is the intake gate.3. **Experiments are gated by math**: sample size from `../experiment-designer/scripts/sample_size_calculator.py`, never gut feel; report the MDE with the verdict.4. **Prioritization shows its framework**: RICE for steady-state, WSJF/cost-of-delay when time sensitivity dominates, opportunity scoring for underserved needs — name which and why (see [references/product_operating_model.md](references/product_operating_model.md)).5. **AI features ship with evals**: a golden set + rubric is the PRD's quality contract for probabilistic features ([references/ai_product_evals.md](references/ai_product_evals.md)).6. **Never modify a gate you are judged by**; exhausted budgets escalate to a named human, never report as success. ## Forcing-question library (grill-with-docs pattern) One per turn, recommended answer, canon citation. Never run a sub-skill or start a loopuntil the lane-defining decision is locked: - **DISCOVERY lane**: "What is the single outcome this discovery serves, stated with a number? Recommended: write it as the OST root first — opportunities without an outcome are a feature factory. Canon: Torres, *Continuous Discovery Habits*; opportunity solution trees (producttalk.org)."- **PRIORITIZE lane**: "Does time sensitivity change this ranking — would delaying any item a quarter erode its value? Recommended: if yes, run WSJF/cost-of-delay alongside RICE and compare ranks; flag items whose rank flips on a one-step estimate change. Canon: Reinertsen, *Principles of Product Development Flow*; SAFe WSJF false-precision critique."- **EXPERIMENT lane**: "What baseline rate and MDE justify this test's runtime? Recommended: compute n first; if you can't reach it in 4 weeks, test a bigger lever. Canon: statistical power analysis (experiment-designer)."- **ANALYTICS lane**: "Is your North Star a leading indicator of value exchange, or revenue/vanity? Recommended: leading value metric with an input tree. Canon: Amplitude, *The North Star Playbook*."- **STRATEGY lane**: "Are these OKRs outcomes or shipping lists? Recommended: outcomes — output OKRs are the #1 operating-model failure. Canon: Cagan, *Transformed* (SVPG, 2024)."- **BUILD lanes (spec-to-repo / saas-scaffolder)**: "Which validated assumption says this should be built at all? Recommended: link the OST test that survived; building is the most expensive way to test an idea. Canon: Torres; Bland, *Testing Business Ideas*." ## Assumptions 1. The user owns (or advises the owner of) the product decision.2. Discovery data lives in the workspace as JSON logs — the loop is file-backed and resumable; every tool ships `--sample` so the shape is visible first.3. The four standalone plugins are installed alongside the bundle (the router still routes to them by path if not). ## Non-goals - Not the delivery loop — sprint/flow/Jira work routes to `project-management`.- Not the generic loop engine — that is `engineering/agent-harness`; this orchestrator is the product-domain adapter (router + discovery gates).- Not campaign marketing — `marketing/landing` builds from-scratch marketing pages; `landing-page-generator` here scaffolds product Next.js/TSX pages. ## Output artifacts | Mode | Artifact ||---|---|| Route | Sub-skill's own artifact + ≤ 200-word digest with one canon-cited challenge || Discovery loop | `discovery_log.json` + cadence report + linted `ost.json` || Harness run | `.agent-harness/plan.json` + `state.json` + close handoff | ## Anti-patterns (do not) - ❌ Run all 16 lanes "to be thorough" — route to one, digest, chain on confirmation- ❌ Cite an OST that fails the linter, or promote a single-participant anecdote to insight- ❌ Ship an AI feature whose PRD has no eval (golden set + rubric)- ❌ Let the discovery streak die silently — DORMANT escalates by name- ❌ Treat RICE as the only prioritization lens when deadlines dominate ## References - [references/continuous_discovery_canon.md](references/continuous_discovery_canon.md) — Torres, OST, assumption testing, JTBD switch interviews, story mapping- [references/product_operating_model.md](references/product_operating_model.md) — Cagan *Transformed*, North Star framework, PLG benchmarks, WSJF/ODI vs RICE- [references/ai_product_evals.md](references/ai_product_evals.md) — evals-as-PRD, model cards, evaluator-optimizer loops- Loop engine: `engineering/agent-harness` · Loop vocabulary: `loop-library`