c-level-advisor/c-level-agents/skills/cpo-review/SKILL.md
/cs:cpo-review <plan> — JTBD-driven interrogation of product roadmap, PMF signal, and portfolio focus. Use when committing a quarter's roadmap, deciding whether to kill a feature, or claiming PMF without a retention curve.
npx skillsauth add alirezarezvani/claude-skills cpo-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
What job is this feature hired to do, in the user's words?
What user behavior does this move, and how does that ladder to the North Star?
What's the retention curve for users who hire this job — is it flat, decaying, or smiling?
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
What gets cut if this ships? Name the specific initiative or feature.
What signal would tell you in 90 days that this was the wrong bet?
python ../../../skills/cpo-advisor/scripts/pmf_scorer.py
python ../../../skills/cpo-advisor/scripts/portfolio_analyzer.py
# 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
/cs:cmo-review — does the positioning support this feature?/cs:execute — build the 90-day plan/cs:post-mortem — if kill criteria triggeredcs-cpo-advisorcpo-advisorproduct-team/skills/product-manager-toolkit/Version: 1.0.0
data-ai
Use when you want to understand what Claude contributed vs what you drove in a session. Triggers on: /collab-proof, session retrospective, ai contribution analysis, collaboration evidence, what did claude do.
data-ai
Personal coach that teaches users to become Claude power users. Use this skill the FIRST time a user asks to "learn Claude", "be a power user", "coach me", "teach me Claude tricks", "what can Claude do", "make me better at prompting", or any variation. After activation, also use it on EVERY subsequent turn to detect missed optimization opportunities (vague prompts, ignored capabilities, manual work Claude could automate) and surface a single power-user tip. Trigger generously — most users do not know what they do not know, so err on the side of coaching.
development
Use when designing or revisiting product pricing — selecting a pricing model (subscription seat-based, usage-based, value-based, freemium, or hybrid), running Van Westendorp Price Sensitivity Meter analysis on WTP survey data, or designing Good/Better/Best packaging tiers. Recommends a model and a price range with trade-offs, never a single number. For Commercial leads, Product Marketing, and CMOs at the pricing-design moment — not deal-by-deal discounting, not brand positioning.
testing
Use when a startup is approached by a prospective partner and someone has to decide should we sign this partner, at what partner tier (referral / reseller / OEM / SI-consulting / strategic alliance), with what joint GTM commitment, and at what revshare. Classifies partner tier from independent-demand evidence vs. preferential-terms hunting, designs a 90-day joint GTM plan, models revshare against direct-sale margin, and surfaces kill criteria for unwinding under-performing partnerships. For Head of Partnerships, Head of BD, and Founder-CEOs doing reseller agreement, OEM deal, or strategic alliance review — not technical sale enablement, not channel cost economics, not M&A.