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Agent skill

Product Skills

by alirezarezvani in alirezarezvani/claude-skills

A skill your agent uses when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery…

MITAuto-check passedFrontend & Design

Install Product Skills

skills CLI
$ npx skills add alirezarezvani/claude-skills --skill product-skills -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alirezarezvani/claude-skills product-skills --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/product-team/skills/product-skills .claude/skills/product-skills && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
product-skills
GitHub stars
28k
Token cost
~2.6k tokens
SKILL.md length
1,028 words
Files
9 (incl. scripts, references, assets)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery…

  • Works in 5 steps: Observe — maintain discovery_log.json… → Choose — the tracker's next_loop_action… → Act — run the interview / assumption… → …
  • Coordinating product work across the 12 bundled product sub-skills (RICE
  • SKILL.md covers When to invoke, Routing logic (deterministic), The discovery loop (the… and Hard rules, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Product Skills is an agent skill from alirezarezvani/claude-skills. 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…

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `assets/sample_discovery_log.json`, `assets/sample_ost.json` and `references/ai_product_evals.md`).

It sits in Frontend & Design, covering OKRs and executive reporting, PRD writing and Landing pages. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • Coordinating product work across the 12 bundled product sub-skills (RICE
  • Competitive teardown
  • SaaS scaffolding)
  • The 4 standalone product-team plugins (user stories

Example prompts

  • “help me prioritize”
  • “plan a product experiment”
  • “we ship features nobody uses”
  • “/product-skills”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Observe — maintain discovery_log.json (interviews, assumption tests; shape in
  2. Choose — the tracker's next_loop_action IS the choice: book the touchpoint,
  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
  5. Record / Repeat-or-stop — update the log, keep the weekly streak alive. Stop

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Product Skills loads about 2.6k tokens when it runs, and up to ~5.5k if it reads all its reference files. Until then it costs about 240 tokens; SKILL.md has 1,028 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~240
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 1,028 words, ~2,599 tokens.

Download SKILL.mdSave it as .claude/skills/product-skills/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
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

SymptomSub-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).
  5. AI features ship with evals: a golden set + rubric is the PRD's quality contract for probabilistic features (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.
Show full SKILL.md (456 more words)Show less

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

ModeArtifact
RouteSub-skill's own artifact + ≤ 200-word digest with one canon-cited challenge
Discovery loopdiscovery_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

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in product-team/skills/product-skills of alirezarezvani/claude-skills.

  • SKILL.md
  • assets/sample_discovery_log.json
  • assets/sample_ost.json
  • references/ai_product_evals.md
  • references/continuous_discovery_canon.md
  • references/product_operating_model.md
  • scripts/discovery_cadence_tracker.py
  • scripts/ost_linter.py
  • scripts/product_goal_router.py

Open the folder on GitHubat commit 19392f7

Compare with similar skills

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Categories

Questions about Product Skills

What does Product Skills do?

A skill your agent uses when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery…. Product Skills is an agent skill from alirezarezvani/claude-skills. 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).

When should I use Product Skills?

Product Skills fits situations like: coordinating product work across the 12 bundled product sub-skills (RICE; competitive teardown; saaS scaffolding); the 4 standalone product-team plugins (user stories.

How do I install Product Skills in Claude Code?

Run `npx skills add alirezarezvani/claude-skills --skill product-skills -a claude-code`. Or copy the skill folder (product-team/skills/product-skills in alirezarezvani/claude-skills) into .claude/skills/product-skills in your project. Claude Code loads it when a task matches its description.

How do I install Product Skills in Codex?

Run `npx skills add alirezarezvani/claude-skills --skill product-skills -a codex`. Or copy the skill folder (product-team/skills/product-skills in alirezarezvani/claude-skills) into .agents/skills/product-skills in your project. Codex loads it when a task matches its description.

Can I use Product Skills in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill product-skills -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-skills, .gemini/skills/product-skills, .github/skills/product-skills and .opencode/skills/product-skills in your project.

What does Product Skills need to run?

Going by SKILL.md and its folder, Product Skills needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Product Skills access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Product Skills safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Product Skills use?

Product Skills is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Product Skills use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.

What are the alternatives to Product Skills?

Skills that share tags, products or a category with Product Skills: Web Design (xiaopu-ai/web-design, 783 stars), Improve Website (wondelai/skills, 2.4k stars), Sveltekit Webapp (LeoYeAI/openclaw-master-skills, 2.2k stars) and Premium SaaS Design (HermeticOrmus/LibreUIUX-Claude-Code, 112 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Skills?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,938 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.