/cs:cpo-review — CPO Forcing Questions
SkillAI & modelsYour AI can put a product plan through a tough, structured review before you commit to it. It asks six hard questions covering jobs-to-be-done, retention, and what to cut from the portfolio. It fits moments like committing a quarter's roadmap, deciding whether to kill a feature, or claiming product-market fit without a retention curve to back it up.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then share the plan you want reviewed — such as a quarterly roadmap or a feature you are considering killing — and ask your AI to run cpo-review on it.
Then ask your AI: use the /cs:cpo-review — CPO Forcing Questions skill
What your AI can do with it
- Put six tough review questions to any product plan
- Probe whether a plan holds up on jobs-to-be-done
- Check that product-market fit claims are backed by a retention curve
- Identify what to cut from a product portfolio
- Pressure-test a quarterly roadmap before committing to it
- Weigh whether a feature is worth killing
What this skill tells your AI
The instructions your AI receives, as published by alirezarezvani/claude-skills in .gemini/skills/cpo-review/SKILL.md and read by ahel’s review.
Command: /cs:cpo-review <plan>
The JTBD-driven builder cuts the roadmap in half. Six questions to surface what to ship and what to kill.
When to Run
- Before quarterly roadmap commitment
- Before launching a new product line
- Before adding > 3 features to a release
- When retention is flat or declining
- When the team is debating "should we build X?"
The Six CPO Questions
1. JTBD
What job is this feature hired to do, in the user's words?
- Not "improve onboarding." "Help a new ops manager get their first deal closed within 7 days."
- Job ≠ feature. Hire ≠ try.
2. North Star Metric
What user behavior does this move, and how does that ladder to the North Star?
- The metric must be leading, behavior-based, and value-correlated.
- If you can't trace the feature to the North Star, don't build it.
3. PMF Signal
What's the retention curve for users who hire this job — is it flat, decaying, or smiling?
- Flat or smiling = PMF signal. Decaying = no PMF.
- "Users like it in surveys" is not a signal.
4. RICE Score
Reach, Impact, Confidence, Effort — what's the score and where does this rank in the queue?
python product-team/skills/product-manager-toolkit/scripts/rice_prioritizer.py
5. Opportunity Cost
What gets cut if this ships? Name the specific initiative or feature.
- Headcount and time are zero-sum. The cut list is the focus list.
6. Kill Criteria
What signal would tell you in 90 days that this was the wrong bet?
- Define the metric and threshold in writing, before launch.
- If you can't define a kill criterion, you can't ship responsibly.
Workflow
- Run the analyses:
python ../../../c-level-advisor/skills/cpo-advisor/scripts/pmf_scorer.py python ../../../c-level-advisor/skills/cpo-advisor/scripts/portfolio_analyzer.py - Answer the six questions.
- Apply the verdict.
Output Format
# CPO Review: <feature/plan>
**Date:** YYYY-MM-DD
## JTBD
> <one sentence in user voice>
## North Star Link
- Metric moved: <name>
- Expected delta: <%>
## PMF Signal
- Retention curve shape: flat / smiling / decaying
- Cohort sample size: N
## Score
- RICE: <number>
- Rank in queue: #N of M
## Cut List
- Cut: <initiative>
- Reason: <why this matters more>
## Kill Criteria (90 days)
- Metric: <name>
- Threshold: <value>
- Action if missed: <kill | iterate>
## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 KILL
Routing
/cs:cmo-review— does the positioning support this feature?/cs:execute— build the 90-day plan/cs:post-mortem— if kill criteria triggered
Related
- Agent:
cs-cpo-advisor - Skill:
cpo-advisor - Execution:
product-team/skills/product-manager-toolkit/
Version: 1.0.0
Signals
- GitHub stars
- 26k
- Forks
- 4k
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
cpo-review- Source
- github.com/alirezarezvani/claude-skills