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)

```bash
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:

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"), compile
through the repo-wide harness instead:

```bash
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

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 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

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`