product-skills

name product-skills description Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing/landing (from-scratch pages), and engineering/agent-harness (the generic loop engine this orchestrator plugs into). context fork version 2.11.1 author Alireza Rezvani license MIT 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 inquiry with scripts/product_goal_router.py across all 16 product-team lanes (12 bundled + 4 standalone plugins), run exactly one, return a digest. Looping: run product work as bounded 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) python3 scripts/product_goal_router.py --text "<the goal>" --output json Exit 0 → route_to names the skill (with skill_path , including the standalone plugins): load its SKILL.md and follow its workflow. Exit 2 → ask ONE clarifying question naming the listed candidates, with a recommended answer. Exit 3 → no signal: ask the user to restate the goal with the deliverable named. Never guess silently; never silently chain — 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 bounded loop with two machine gates: Observe — maintain discovery_log.json (interviews, assumption tests; shape in assets/sample_discovery_log.json ) and score the cadence: 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 . 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). Act — run the interview / assumption test with the routed sub-skill's tools. Verify — keep the tree structurally sound before it may drive a roadmap: 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). 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"), compile through the repo-wide harness instead: python3 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 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). Outcome-first : every loop hangs from one measurable outcome — the linter's O1 rule is the intake gate. Experiments are gated by math : sample size from ../experiment-designer/scripts/sample_size_calculator.py , never gut feel; report the MDE with the verdict. 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 ). AI features ship with evals : a golden set + rubric is the PRD's quality contract for probabilistic features ( references/ai_product_evals.md ). 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 loop until 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 The user owns (or advises the owner of) the product decision. 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. 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 — Torres, OST, assumption testing, JTBD switch interviews, story mapping references/product_operating_model.md — Cagan Transformed , North Star framework, PLG benchmarks, WSJF/ODI vs RICE references/ai_product_evals.md — evals-as-PRD, model cards, evaluator-optimizer loops Loop engine: engineering/agent-harness · Loop vocabulary: loop-library