identity-snapshot

---
name: identity-snapshot
description: Generate a living identity model from existing Dex data — working patterns, decision tendencies, quality preferences, and growth areas.
---

# Identity Snapshot

Reads existing Dex data and synthesizes `System/identity-model.md` — a living document the system writes, not the user. This captures who you are as a professional based on your actual behavior, not self-reported traits.

## When to Run

- Manually via `/identity-snapshot`
- Auto-triggered during `/week-review` (append identity model update to review workflow)

## Data Sources

Read ALL of the following in parallel before synthesizing:

1. **Quarter Goals** — `01-Quarter_Goals/Quarter_Goals.md`
   - What you're working toward, pillar distribution, ambition level
2. **Week Priorities** — `02-Week_Priorities/Week_Priorities.md`
   - Recent 4 weeks of priorities (look for patterns in what gets prioritized)
3. **Tasks** — `03-Tasks/Tasks.md`
   - Completion patterns, pillar distribution, velocity, what gets blocked
4. **Session Learnings** — `System/Session_Learnings/*.md` (last 30 days)
   - What you've learned, recurring themes
5. **Mistake Patterns** — `06-Resources/Learnings/Mistake_Patterns.md`
   - Known failure modes, triggers, what to watch for
6. **Skill Ratings** — `System/Skill_Ratings/ratings.jsonl`
   - Which skills score highest, what you value in AI interactions
7. **User Profile** — `System/user-profile.yaml`
   - Existing identity data, communication preferences

## Synthesis

Write to `System/identity-model.md` with this structure:

```markdown
# Identity Model
*Auto-generated by Dex — last updated YYYY-MM-DD*

## Working Patterns
- Pillar balance: [distribution across pillars]
- Priority cadence: [how often priorities shift, what stays stable]
- Task velocity: [completion rate, average items per week]
- Peak focus areas: [what dominates recent weeks]

## Decision Tendencies
- Under pressure: [what gets prioritized vs deprioritized]
- Goal selection: [ambitious vs tactical, how goals evolve]
- Time allocation: [deep work vs meetings vs quick tasks]

## Quality Preferences
- Highest-rated skills: [from ratings.jsonl]
- What "good" looks like: [patterns from high ratings + notes]
- What frustrates: [patterns from low ratings + mistake patterns]

## Growth Areas
- Recurring gaps: [from mistake patterns, things that keep coming up]
- Skills under development: [from learnings, new capabilities being adopted]
- Blind spots: [pillars or areas consistently neglected]

## Communication Style
- Formality: [from profile + observed patterns]
- Decision speed: [fast/deliberate based on task patterns]
- Feedback preference: [from profile]
```

## Rules

- **Never ask the user for input.** This is purely observational.
- **Be specific.** Use actual data points, not generic statements.
- **Be honest.** If a pillar is neglected, say so. If velocity dropped, note it.
- **Date-stamp every generation.** The model should show evolution over time.
- If any data source is missing or empty, note it as "[No data available]" and move on.