Agent skill

decision-journal

Unified decision lifecycle: Pre-decision logging, post-decision review, failure classification, and calibration tracking. Absorbs: post-mortem-engine.

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Install this agent skill to your Project

npx add-skill https://github.com/winstonkoh87/Athena-Public/tree/main/examples/skills/decision/decision-journal

SKILL.md

Decision Engine (Journal + Post-Mortem)

Absorbs: post-mortem-engine

Complete decision lifecycle in one skill: record decisions BEFORE outcomes are known, review them AFTER, classify failures objectively, and track calibration over time.

Triggers

"I've decided to", "logging a decision", "was that a good decision", "calibration", "what went wrong", "post mortem", "failure analysis", "AAR", "I screwed up"

Part 1: Pre-Decision Entry (BEFORE outcome)

markdown
## Decision Entry: [YYYY-MM-DD HH:MM]

### The Decision
[What am I deciding to do?]

### The Alternatives
1. [Alternative A and why I rejected it]
2. [Alternative B and why I rejected it]

### My Confidence
[X]% confident this is the right call.

### Key Assumptions (numbered)
1. [Assumption 1]
2. [Assumption 2]

### What Would Change My Mind
[Specific observable evidence that would make me reverse]

### Expected Outcome
- Best case: [description] (probability: X%)
- Most likely: [description] (probability: X%)
- Worst case: [description] (probability: X%)

### Decision Class
- [ ] Reversible (Type 2 — decide fast, adjust later)
- [ ] Irreversible (Type 1 — decide carefully, no undo)

Part 2: Post-Decision Review (30-90 days later)

markdown
## Review: [Original Decision Date]

### Actual Outcome
[What actually happened?]

### Assumptions Audit
1. [Assumption 1]: [Correct / Wrong / Partially correct]
2. [Assumption 2]: [Correct / Wrong / Partially correct]

### Calibration
- Stated confidence: X%
- Would I make the same decision with same info? [Yes / No]
- Outcome due to: [good decision / luck / bad decision / bad luck]

Part 3: Post-Mortem (When Things Go Wrong)

Phase 1: Just the Facts (No Interpretation)

Timeline of observable events only. No opinions, no "I should have."

Phase 2: Root Cause (The 5 Whys)

1. Why did [outcome] happen? → Because [cause 1]
2. Why? → Because [cause 2]
3. Why? → Because [cause 3]
4. Why? → Because [cause 4]
5. Why? → Because [ROOT CAUSE]

Phase 3: Classification

Category Question
Process Failure Followed system, it failed → Update system
Execution Failure Deviated from system → Discipline issue
Information Failure Critical info unavailable → Update model, not system
Luck Failure Within expected failure rate → Change NOTHING

Critical Rule: Luck failures do NOT get process changes. At 60% WR, 40% of trades WILL fail. Changing your process after a luck failure is the #1 way to destroy a working edge.

Output

Post-Mortem Report: [Event]
─────────────────────────────
Root Cause: [One sentence]
Classification: [PROCESS / EXECUTION / INFORMATION / LUCK]
Required Changes: [Specific actions or "NONE — within expected parameters"]

Calibration Tracking

Over 20+ reviewed decisions:

Stated Confidence Actual Correct % Calibration
90% XX% [Over / Under / Well-calibrated]
70% XX% [Over / Under / Well-calibrated]
50% XX% [Over / Under / Well-calibrated]

Storage: .context/memories/decision_journal/

Integration

  • Triggers trading-risk-gate on Type 1 (irreversible) decisions
  • Feeds into trade-journal-analyzer for trading decisions
  • Triggers circuit-breaker if process failures are recurring

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