User Model Update

SKILL.md

---
name: user-model-update
description: Update user-model.md using Memory-as-Reasoning principles. Triggered on corrections, contradictions, or session wrap-up. Uses logical reasoning (deductive, inductive, abductive) to derive predictions from observations.
---

# User Model Update Skill

Based on [Plastic Labs "Memory as Reasoning"](https://blog.plasticlabs.ai/blog/Memory-as-Reasoning)

## Core Principle

**Memory is not static storage. Memory is prediction.**

Don't store facts. Make predictions about behavior and update them when surprised.

---

## When to Trigger

| Trigger | Signal | Priority |
|---------|--------|----------|
| **Correction** | User explicitly corrects a behavior/assumption | HIGH - immediate |
| **Contradiction** | New observation conflicts with existing prediction | HIGH - immediate |
| **Pattern emergence** | 3+ similar observations form a pattern | MEDIUM - batch |
| **Session wrap-up** | End of substantial session | LOW - review |

---

## The Reasoning Protocol

### Step 1: Classify the Observation

What type of input is this?
- **Explicit statement** - User directly stated a preference/fact
- **Behavioral signal** - Action that implies preference
- **Correction** - User corrected agent behavior
- **Contradiction** - Conflicts with existing model

### Step 2: Apply Logical Reasoning

Use all three types:

**Deductive (certain, from explicit premises):**
```
Premise: Henry said "never use em dashes"
Premise: Em dashes are a punctuation choice
Conclusion: Henry has explicit punctuation preferences
Certainty: HIGH (directly stated)
```

**Inductive (patterns from observations):**
```
Observation: 50+ sessions, Henry always leads with task
Observation: No small talk in any session
Observation: Responds faster to direct messages
Conclusion: Henry prefers task-first communication
Certainty: HIGH (consistent pattern)
```

**Abductive (best explanation for behavior):**
```
Observation: Henry works at 3am regularly
Observation: Never mentions being tired
Observation: Rejects suggestions to rest
Best explanation: Work is energizing, not draining for him
Prediction: Comments about late hours will annoy him
Certainty: MEDIUM (inferred, not stated)
```

### Step 3: Derive Implications

For each conclusion, ask:
1. What should the agent DO differently?
2. What should the agent AVOID?
3. What predictions can we make about future behavior?

Format:
```
Derived Implications:
- DO: [specific action]
- AVOID: [specific anti-pattern]
- PREDICT: [expected future behavior]
```

### Step 4: Check for Contradictions

Compare new conclusion against existing `user-model.md`:
- Does it conflict with any existing prediction?
- If yes: Which has stronger evidence?
- Resolution: Update the weaker, log the change

**Handle contradictions without "neural inertia":**
- Don't resist updates just because old belief existed longer
- Evidence strength > belief age
- Log the reasoning for the change

### Step 5: Update user-model.md

Add/modify the relevant section:

```markdown
### [Category] (Certainty: HIGH/MEDIUM/LOW)
**Observation:** [What was observed]
**Evidence:** [Supporting data points]
**Reasoning:** [Deductive/Inductive/Abductive logic applied]
**Derived Implications:**
- DO: [action]
- AVOID: [anti-pattern]
- PREDICT: [future behavior]
```

### Step 6: Log to Reasoning Trace

Append to the "Reasoning Trace Log" section:

```markdown
### [DATE] - [Category] Update
- **Trigger:** [correction/contradiction/pattern/session]
- **Previous:** [old prediction if any]
- **New evidence:** [what was observed]
- **Reasoning:** [brief logic chain]
- **Updated:** [what changed]
- **Certainty change:** [if applicable]
```

---

## Certainty Levels

| Level | Criteria | Update Threshold |
|-------|----------|------------------|
| **HIGH** | Explicit statement OR 10+ consistent observations | Only contradicted by explicit statement |
| **MEDIUM** | 3-9 observations OR strong abductive inference | New pattern (5+ obs) or explicit statement |
| **LOW** | 1-2 observations OR weak inference | Any contradicting evidence |

---

## Surprisal = Signal

When predictions are WRONG, that's valuable:
1. Log the failed prediction
2. Analyze why it failed
3. Update the model
4. Add to "Predictions to Validate" if uncertain

**Errors improve the model. Don't hide them.**

---

## Example Update Flow

**Trigger:** Henry corrects: "don't send markdown tables to Telegram"

**Step 1 - Classify:** Explicit correction (HIGH priority)

**Step 2 - Reason:**
- Deductive: Henry said don't do X → X is wrong
- Abductive: Why? Tables render poorly in Telegram → visual clarity matters

**Step 3 - Derive:**
- DO: Use PNG (tablesnap) for tables
- AVOID: Markdown tables in Telegram
- PREDICT: Will prefer visual formats for complex data

**Step 4 - Check Contradictions:** No existing table preference → new entry

**Step 5 - Update user-model.md:** Add to Communication Patterns

**Step 6 - Log:**
```
### 2026-01-31 - Visual Data Update
- **Trigger:** Explicit correction
- **New evidence:** "don't send markdown tables to Telegram"
- **Reasoning:** Tables render poorly → visual clarity preference
- **Updated:** Added "Visual Over Text" prediction (HIGH)
```

---

## Integration Points

### SOUL.md Rule
Add to Core Truths or Things You DON'T Do:
```
When corrected by Henry → Immediately run user-model-update skill
```

### Session Wrap-up
Add to wrap-up checklist:
```
- [ ] Any new user-model learnings this session?
- [ ] Any predictions that failed?
```

### Weekly Cron (Optional)
Schedule review of recent sessions to extract patterns missed in real-time.

---

## Anti-Patterns

❌ **Static storage:** "Henry likes X" (no reasoning, no implications)
❌ **Arbitrary certainty:** "Confidence: 0.73" (meaningless numbers)
❌ **Belief resistance:** Keeping old predictions despite new evidence
❌ **Hidden errors:** Not logging failed predictions
❌ **Orphan facts:** Conclusions with no derived implications

---

## Files

- **user-model.md** - The reasoning-based model (predictions + evidence)
- **USER.md** - Static biographical facts (name, timezone, accounts)

USER.md = who Henry IS (facts)
user-model.md = how Henry BEHAVES (predictions)

---

*"The question isn't how best to store your data, but how best to reason over it."*
— Plastic Labs