self-review
Write a performance self-review that's specific, evidenced, and balanced. Use when asked to write a self-review, self-assessment, or self-evaluation for a performance cycle. Produces a complete self-review — accomplishments mapped to impact and competencies, growth areas owned honestly, and a forward-looking development plan, in the voice of the person being reviewed.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/self-review && cp -r /tmp/self-review/plugins/pm-career/skills/self-review ~/.claude/skills/self-reviewSKILL.md
# Self-Review Skill
A self-review is your one chance to frame your own year before someone else does. Done badly it's a
vague list of activities; done well it's an evidenced narrative that maps your work to the competencies
you're measured on, owns growth honestly, and sets up the next level. This skill writes that — pulling
straight from a [`brag-doc`](../brag-doc/SKILL.md) if you have one.
## Required Inputs
Ask for these only if they aren't already provided:
- **Your role, level, and the review period.**
- **Accomplishments** — your wins with impact/metrics (or point to a brag doc).
- **The competency framework / rating dimensions** you're assessed on (if any).
- **Growth areas** — where you fell short or want to develop (be honest; reviewers trust self-awareness).
- **Goals** for the next period.
## Output Format
### Self-Review — [name], [role], [period]
**1. Summary** — 3–4 sentences: the headline of your period and the through-line. Lead with impact.
**2. Key accomplishments** — your top 3–6, each as **outcome → your contribution → evidence → which competency it demonstrates**. Quantify; tie to team/company goals.
**3. Strengths** — the 2–3 competencies you most demonstrated, with the proof.
**4. Growth areas** — 1–3, owned plainly: what was hard, what you learned, what you're changing. This section *builds* credibility when it's specific and non-defensive (not "I work too hard").
**5. Goals & development plan** — what you'll focus on next period and the support you need.
**6. Rating rationale** *(if self-rating)* — the rating you'd give and the evidence for it, calibrated to the framework — not inflated, not falsely modest.
## Quality Checks
- [ ] Accomplishments are quantified and tied to the competency framework / company goals
- [ ] Each claim is backed by specific evidence, not adjectives
- [ ] Growth areas are genuine and specific (not humble-brags), with what you're doing about them
- [ ] The narrative has a through-line, not just a list
- [ ] A self-rating (if used) is calibrated to the rubric with evidence — defensible, not aspirational
## Anti-Patterns
- [ ] Do not list activities — map every accomplishment to an outcome and a competency
- [ ] Do not disguise a strength as a weakness ("too detail-oriented") — it reads as evasive; name a real growth area
- [ ] Do not claim team wins as solo, or undersell your role out of modesty — be precise about your contribution
- [ ] Do not inflate the self-rating beyond what the evidence supports — it costs credibility in calibration
- [ ] Do not write in vague superlatives — "drove significant impact" means nothing without the number
## Based On
Competency-based performance-review practice — evidence-mapped accomplishments and calibrated self-assessment.Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.