# Decision Review > Review decision log entries that are due: compare expected vs. actual outcome, assess calibration error, extract lessons. Trigger when the user says "review decisions", "review the decisions that are due", "decision-review", or when `make check` flags decisions that are due. - Skill: `kelvinyou/decision-review` (Agent Skill) - Install (CLI): `npx skillmds@latest add kelvinyou/decision-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/kelvinyou/decision-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: KelvinYou (https://skillmd.com/u/kelvinyou) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kelvinyou/decision-review --- # Decision Review Agent — Personal-OS Walks the user through reviewing due decisions, to calibrate their judgment. ## Core principles - **`expected_outcome` is immutable**: prevents post-hoc rationalization. During review, show the original text first, then guide the user to write the actual outcome - **Neutral narrative**: don't judge with "you guessed wrong" — frame it as "the actual result deviated from expectation, what's the signal here" - **Push mechanism**: if the outcome isn't clear yet, allow deferring rather than forcing a judgment ## Workflow ### Step 1: Find due decisions ```bash # List all due decisions .venv/bin/python3 scripts/decisions_due.py ``` If the user specified an ID (`$ARGUMENTS`), review only that one. Otherwise review every due decision in turn. ### Step 2: Review each one For each due decision: 1. **Read the decision file**: `data/decisions/.md` 2. **Show the original record** (read-only, not editable): ``` 📋 Decision: - date_decided: YYYY-MM-DD - category: ... - stakes: ... - decision_type: ... - expected_outcome: - context: ``` 3. **Guide the user through these questions**: - "What actually happened? (one line)" - "Compared to the expectation: as_expected / better / worse / too_early / irrelevant?" - "Any lessons? (one or two sentences, optional)" - If the user wants to fill in a confidence value: "Looking back, how confident were you in this decision at the time? (0.0-1.0)" 4. **Handle `too_early`**: - If the user selects `too_early`, auto-push: - `status` → `pushed` - `review_date` += 30d - `calibration_delta` → `too_early` - Tell the user the new review date 5. **Handle a normal review**: - Write `actual_outcome`, `calibration_delta`, `lesson` - If the user provided `confidence`, write that field - `status` → `reviewed` ### Step 3: Write to file Use the Edit tool to update the decision file's YAML frontmatter. Only modify the review fields, leave everything else untouched. ### Step 4: Summarize Once all decisions are reviewed, output a summary: ``` 📊 Review summary: - reviewed: N - pushed (too_early): M - as_expected: X | better: Y | worse: Z | irrelevant: W Next decision due: (YYYY-MM-DD) ``` ## Write rules - **Only write**: `actual_outcome`, `calibration_delta`, `lesson`, `confidence` (optional), `status`, `review_date` (only when pushed) - **Never modify**: `expected_outcome`, `category`, `stakes`, `decision_type`, `date_decided`, or the body content - After writing, tell the user the file path ## Out of scope - Never modify `expected_outcome` (immutable) - Never give advice for a new decision - Never compute the Brier score (that's `calibration.py`'s job) - Never modify the daily log or weekly report