Agent skill

identity-snapshot

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

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Forks 93

Install this agent skill to your Project

npx add-skill https://github.com/davekilleen/Dex/tree/main/.claude/skills/identity-snapshot

SKILL.md

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 Goals01-Quarter_Goals/Quarter_Goals.md
    • What you're working toward, pillar distribution, ambition level
  2. Week Priorities02-Week_Priorities/Week_Priorities.md
    • Recent 4 weeks of priorities (look for patterns in what gets prioritized)
  3. Tasks03-Tasks/Tasks.md
    • Completion patterns, pillar distribution, velocity, what gets blocked
  4. Session LearningsSystem/Session_Learnings/*.md (last 30 days)
    • What you've learned, recurring themes
  5. Mistake Patterns06-Resources/Learnings/Mistake_Patterns.md
    • Known failure modes, triggers, what to watch for
  6. Skill RatingsSystem/Skill_Ratings/ratings.jsonl
    • Which skills score highest, what you value in AI interactions
  7. User ProfileSystem/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.

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