phuryn/pm-skills is a marketplace of reusable skills, commands, and plugins that guide AI assistants through product-management work such as discovery, strategy, planning, metrics, launches, and growth. It is for product managers and teams using Claude Code, Cowork, or compatible assistants. The catalogue entries are the project's own workflows and extensions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/phuryn/pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/phuryn/pm-skills/setup-metrics)<a href="https://agentmods.dev/commands/phuryn/pm-skills/setup-metrics"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/setup-metrics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/phuryn/pm-skills/setup-metrics"><img src="https://agentmods.dev/badge/commands/phuryn/pm-skills/setup-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.00999 |
| Opus 5 | $0.00010 | $0.00500 |
| Sonnet 5 | $0.00004 | $0.00200 |
| Haiku 4.5 | $0.00002 | $0.00100 |
Grade A, and why
setup-metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup-metrics -- Product Metrics Dashboard Design
Design a comprehensive metrics framework for your product or feature — from selecting the right North Star to defining alert thresholds that catch problems early.
Invocation
/setup-metrics SaaS project management tool
/setup-metrics New checkout flow we just launched
/setup-metrics # asks what you're measuring
Workflow
Step 1: Understand What to Measure
Ask the user:
- What product or feature area are you setting up metrics for?
- What stage is it in? (pre-launch, recently launched, mature)
- What are the current business goals or OKRs?
- Do you have existing metrics? What's missing or broken?
- What analytics tools are you using? (helps tailor implementation advice)
Step 2: Define the Metrics Framework
Apply the metrics-dashboard skill:
North Star Metric:
- Identify the single metric that best captures the value your product delivers to users
- Validate against criteria: measures value delivery, is a leading indicator, is actionable
- Define the metric precisely (formula, data source, time window)
Input Metrics (3-5):
- Identify the levers that drive the North Star
- Each input metric should be directly actionable by a team
- Map the causal chain: Input → North Star → Business Outcome
Health Metrics (3-5):
- Metrics that should stay stable — if they degrade, something is wrong
- Examples: error rates, latency, support ticket volume, NPS, churn rate
- Define "healthy" ranges and degradation thresholds
Counter-Metrics (1-2):
- Metrics that could indicate you're optimizing the wrong way
- Example: if North Star is "daily active users", counter-metric is "session quality" to prevent empty engagement
Step 3: Design Alert Thresholds
For each metric:
| Metric | Green | Yellow | Red | Check Frequency |
|---|---|---|---|---|
| [metric] | [healthy range] | [warning] | [critical] | [daily/weekly] |
- Yellow: Investigate — something may be off
- Red: Act immediately — page someone or escalate
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 120 lines · 19 tokens per session scan A f427356ee43a
setup-metrics is a command published in the GitHub repository phuryn/pm-skills (26,507 stars, last pushed 7d ago), licensed MIT. It adds 19 tokens to every session and 999 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
Other commands, from other repositories
retro
Facilitate a post-launch or sprint retrospective anchored to OKRs.
discover
Run a full discovery cycle — problem framing, JTBD demand-side analysis, assumption mapping, opportunity sizing, and OST mapping — from a rough idea to validated opportunity.
plan-sprint
Plan the upcoming sprint — epic breakdown, user story decomposition, RICE prioritization, dependency check, and capacity allocation.
roadmap
Build or review your roadmap — OKR alignment, Now/Next/Later structuring, dependency mapping, and stakeholder views, pulling live Linear/Jira state.
set-okrs
Structure OKRs — define objectives, write measurable key results, align to strategy, stress-test for quality, and cascade across teams.
stakeholder-update
Generate tailored stakeholder updates by audience — pulls live tracker state from Linear/Jira, formats by audience (exec / engineering / customer) using Pyramid Principle.