outcome-tracker
Record the testable predictions inside a decision, then score them against reality later — so frameworks earn trust from outcomes, not vibes. Use when committing to a prioritisation, forecast, or plan (to log what it predicts), when asked to review what actually happened, or to compute how well-calibrated past RICE scores, forecasts, or bets have been. Produces a prediction record at decision time, and a calibration report with per-framework hit rates at review time.
npx skills add mohitagw15856/pm-claude-skills --skill outcome-tracker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Outcome Tracker Skill Every prioritisation, forecast, and launch plan makes predictions — then everyone forgets to check them. This skill closes the loop: extract the predictions at decision time, park them somewhere durable, and score them against reality on a schedule. Over time it answers the question no one can answer today: *which of our frameworks actually predict outcomes?* ## What This Skill Produces - **At decision time:** a prediction record — each claim made falsifiable, with a metric, a direction/target, a check-by date, and a stated confidence - **At review time:** an outcome scoring of due predictions (hit / miss / partial / unresolvable), with what was learned - **On demand:** a calibration report — per-framework and per-confidence-band hit rates from the accumulated records ## Required Inputs Ask for (if not already provided): - **Mode** — record (new decision), review (score due predictions), or calibrate (analyse the history) - **Record mode:** the decision artifact (RICE table, forecast, launch plan, OKR set) and where records live (a `predictions/` folder in the Brain, or a JSON/markdown file in the repo) - **Review mode:** the stored predictions plus current
- What This Skill Produces
- Required Inputs
- Making Claims Falsifiable (record mode)
- Scoring (review mode)
- Programmatic Helper
- Brain Integration
- Output Format
- Predictions registered: [decision] — [date]
- Outcome review — [date]
- Quality Checks
- Anti-Patterns
python3 scripts/outcome_calibration.py predictions.json
echo '[{"framework":"rice-prioritisation","confidence":0.8,"outcome":"hit"}]' | python3 scripts/outcome_calibration.py -What does the outcome-tracker skill do?
Record the testable predictions inside a decision, then score them against reality later — so frameworks earn trust from outcomes, not vibes. Use when committing to a prioritisation, forecast, or plan (to log what it predicts), when asked to review what actually happened, or to compute how well-calibrated past RICE scores, forecasts, or bets have been. Produces a prediction record at decision time, and a calibration report with per-framework hit rates at review time.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill outcome-tracker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.