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 metric values for the due ones
• Calibrate mode: the prediction history (the calculator below reads it as JSON)

Making Claims Falsifiable (record mode)

Walk the artifact and force each implicit claim into this shape — a prediction that can't fill the row doesn't get recorded, it gets flagged as untestable:

| Field | Rule | |---|---| | claim | One sentence, future tense, about a measurable effect ("onboarding redesign lifts activation") | | metric | The exact instrumented metric, with today's baseline | | predicted | Direction + magnitude band ("+10-20% relative") — bands beat point estimates | | confidence | 0.5–0.95, from the author, recorded before the outcome is knowable | | check_by | The date the effect should be visible if real; also the review trigger | | framework | What produced the claim (rice-prioritisation, gut call, sales-forecasting-model…) — this is what calibration is about |

Typical yields: a RICE table → one prediction per top-3 item (impact claims); a forecast → the quarter's number; a launch plan → its success metrics; an OKR set → each KR's target.

Scoring (review mode)

For each prediction past its check_by: hit (actual within the predicted band), partial (right direction, wrong magnitude), miss (wrong direction or no effect), unresolvable (metric never instrumented, or confounded by a simultaneous change — record why; a pile of unresolvables is itself a finding about how the team instruments its bets). Never rescore or reinterpret the original claim to make it a hit — the record is append-only.

Programmatic Helper

scripts/outcome_calibration.py (stdlib-only) computes the calibration report from a JSON array of prediction records:

bash
python3 scripts/outcome_calibration.py predictions.json
echo '[{"framework":"rice-prioritisation","confidence":0.8,"outcome":"hit"}]' | python3 scripts/outcome_calibration.py -

It reports per-framework hit rates (hits + half-credit partials over resolved), per-confidence-band calibration (do 80%-confidence claims land ~80% of the time?), and flags overconfident bands. Use the computed numbers; don't estimate them.

Brain Integration

If a professional-brain (brain/) exists, records live in brain/predictions/<id>.md (one file per prediction, fields as frontmatter, [hunch]/[data] provenance on the baseline) and review mode starts by listing files with check_by in the past. Pair with schedule-recipe to run review mode monthly — outcome tracking only works as a ritual, not an intention.

Output Format

Record mode:

Predictions registered: decision] — date]

| # | Claim | Metric (baseline) | Predicted | Confidence | Check by | Framework | |---|---|---|---|---|---|---| Untestable claims flagged: claim → what instrumentation would make it testable]

Review mode:

Outcome review — date]

| # | Claim | Predicted | Actual | Outcome | Learning | |---|---|---|---|---|---| Now due next: next check_by dates]

Calibrate mode: the calculator's report plus 2-3 sentences of interpretation — which framework has earned trust, where the team is overconfident, and the single instrumentation fix that would resolve the most unresolvables.

Quality Checks

• ] Every recorded prediction has all six fields — no "improve activation" without a metric, band, and date
• ] Confidence was stated before the outcome was knowable, never backfilled
• ] Review scored every due prediction, including the embarrassing ones — no silent skips
• ] Unresolvables carry a reason, and the calibration report counts them separately from misses
• ] Calibration numbers come from the calculator, not estimation

Anti-Patterns

• ] Do not reinterpret a claim after the fact so it scores as a hit — the original wording is the contract
• ] Do not record point estimates when the author thinks in ranges — bands are honest, points are theatre
• ] Do not let a framework take credit for hits and blame "execution" for misses — score the prediction as made
• ] Do not compute calibration on fewer than ~10 resolved predictions per framework — report "insufficient history" instead
• ] Do not skip recording because the decision feels obvious — obvious bets that miss are the most valuable calibration data