brag-doc
Keep a running brag document of your accomplishments so reviews and promo cases write themselves. Use when asked to start or update a brag doc, log a win, track accomplishments, or prep evidence for a review/promotion. Produces a structured, dated accomplishment log — impact-first entries with metrics, scope, and the evidence link — grouped so it drops straight into a self-review or promo packet.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/brag-doc && cp -r /tmp/brag-doc/plugins/pm-career/skills/brag-doc ~/.claude/skills/brag-docSKILL.md
# Brag Doc Skill
Nobody remembers in December what they shipped in March — so good work goes uncredited at review time.
A brag doc is the fix: a running, dated log of what you did and the impact it had, captured while it's
fresh. This skill turns a pile of "stuff I did" into impact-first entries you can paste straight into a
[`self-review`](../self-review/SKILL.md) or [`promotion-packet`](../promotion-packet/SKILL.md).
## Required Inputs
Ask for these only if they aren't already provided:
- **The win(s)** — what you did (rough notes are fine; the skill structures them).
- **Impact** — the outcome and any metric (before → after, time saved, revenue, users) — even a rough one.
- **Scope & role** — your specific contribution vs. the team's, and who it affected.
- **Date / period** and any **evidence** (PR, doc, dashboard, kudos, ticket link).
## Output Format
### Brag Doc — [your name], [period]
Entries newest-first, grouped by theme (or quarter). Each entry is **impact-first**:
> **[Verb-led headline — the outcome, not the task]** · *[date]*
> - **What I did:** [the specific action and your role in it]
> - **Impact:** [metric / outcome — before → after where possible]
> - **Scope:** [who/what it affected — team, org, customers]
> - **Evidence:** [link]
> - **Maps to:** [the competency / ladder level it demonstrates — e.g. "cross-team influence"]
Example:
> **Cut onboarding drop-off 18% → 9%, unlocking ~$140k ARR** · *Mar 2026*
> - **What I did:** led the redesign of the 3-step signup flow; wrote the PRD, drove eng + design alignment.
> - **Impact:** activation 41% → 52%; drop-off halved (measured over 6 wks, 20k users).
> - **Scope:** owned end-to-end; affected all new signups.
> - **Evidence:** [PRD] · [dashboard]
> - **Maps to:** drives measurable product outcomes; cross-functional leadership.
End with a **"Themes this period"** summary — the 3–4 narrative threads your wins ladder up to.
## Quality Checks
- [ ] Every entry leads with **impact/outcome**, not the activity
- [ ] Metrics include the baseline (before → after), not a bare percentage
- [ ] Your specific contribution is distinguished from the team's
- [ ] Each entry links real evidence
- [ ] Entries are tagged to a competency/ladder level, so the doc feeds a review or promo case directly
## Anti-Patterns
- [ ] Do not log tasks ("attended planning", "wrote code") — log outcomes ("shipped X, which moved Y")
- [ ] Do not wait until review season — capture wins within a week, while the metrics and context are fresh
- [ ] Do not inflate or claim team wins as solo — overstated credit is worse than none when a manager checks
- [ ] Do not omit the metric because it's imperfect — a rough, labelled estimate beats "improved things"
- [ ] Do not bury the evidence — an unlinked claim is one a busy manager can't verify or champion
## Based On
Brag-document practice (Julia Evans) and impact-first accomplishment tracking.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.