Introspect

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name: introspect description: Analyze your Claude Code sessions to discover your developer DNA - gamified performance report with letter grades, archetypes, behavioral patterns, shadow archetype, chrono analysis, and growth tips. Use when user says introspect, analyze my sessions, how do I use Claude, session report, dev stats, my developer DNA, session analysis, how am I performing, or developer profile.

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Introspect 🔍 — Discover Your Developer DNA

Installation

clawhub install introspect --dir ~/.claude/skills

That's it. Works on Linux, macOS, and Android (Termux). Requires Claude Code with ~/.claude/ directory.

How It Works

Hybrid architecture: Python extracts raw session data, Claude (the AI) interprets patterns and writes a personalized analysis report. Not a template filler - every report is uniquely written by Claude based on your actual conversations.

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> You are a developer psychologist. Not a calculator, not a template filler. You READ the actual session data, THINK about patterns, and WRITE a genuine, personalized analysis. Your report should feel like a session with a sharp, funny, insightful coach — not a printout from a machine.

Phase 1: Collect Input

Ask the user: 1. Date range: "How many days back?" (default: 7) 2. Session count: "How many sessions to pick?" (default: 10, max: 50) 3. Project filter: "Specific project or all?" (default: all)

Phase 2: Extract Data

Run the data extraction script:

python3 ~/.claude/skills/introspect/scripts/analyze.py \
  --days  \
  --sessions  \
  --project  \
  --output ~/.claude/skills/introspect/reports/

This outputs a JSON file with raw metrics, session snippets, chrono data, and conversation samples.

Read the generated JSON file — it contains everything you need for Phase 3.

Phase 3: Analyze & Interpret (YOUR JOB — THE BRAIN)

Read the JSON data. Then actually analyze it. Don't just convert numbers to grades — THINK about what the patterns mean.

3.1 — Score Parameters (S/A/B/C/D)

Score each parameter using both the raw metrics AND your reading of the session snippets:

| Param | Raw Data | Your Interpretation | |-------|----------|-------------------| | 🎯 Clarity | prompt length distribution, short/mega counts | Read the actual first messages — are they clear? Would YOU understand what was needed? | | 🔄 Iteration Efficiency | median turns, repeated prompts | Don't blindly penalize high turns. High turns + high tool usage + deliverable = productive complex session. High turns + high frustration + no deliverable = spinning wheels. Context matters — read the snippets before scoring. | | 🧩 Decomposition | mega prompts, structured prompts, scope analysis | Did the user plan and break things down, or dump everything at once? | | ~~🏃 Momentum~~ | ~~completion rate~~ | REMOVED as graded metric. Session endings are NOT a fair measure — users restart sessions for context recovery, work across sessions, or simply move on because they know what they did. Completion data is available in the JSON for context/fun stats but should NOT be graded. | | 🛠️ Tool Leverage | tool calls, unique tools, sessions with tools | Is the user letting Claude work (exec, write, read) or just chatting? | | 😤 Frustration Recovery | frustration_strong, frustration_mild, repeated prompts | Strong frustration (multi-word phrases like "still not working", "you broke it") is clear. Mild signals ("no", "wrong") are only counted when they appear in PATTERNS (2+ in recent messages). Single "no" = correction, not frustration. Read the actual frustration moments in snippets before scoring. | | 👁️ Verification | verification signals count | Did the user verify meaningfully or just say "run tests" without understanding? | | 💬 Engagement | engagement vs blind agreement vs informed_checkpoint counts | IMPORTANT: The JSON now distinguishes between blind_agreement (user said "ok" when Claude just outputted work) and informed_checkpoint (user said "continue" because Claude ASKED "should I continue?"). Informed checkpoints are GOOD — token-efficient responses to prompted questions. Only blind_agreement indicates passive consumption. | | 📊 Token Efficiency | tokens per turn, total tokens | Context: high tokens might be fine for complex tasks, wasteful for simple ones | | 🧠 Cognitive Load Management | files touched, branches, tools per session | Context matters: High file count + single branch + related files = complex but focused work (GOOD). High file count + multiple branches + unrelated files = scattered (needs work). Don't penalize complexity — penalize lack of structure within complexity. | | 🔀 Context Switching | projects per day, fragmentation data | Focused deep work or scattered multi-tasking? | | 🎯 Goal Clarity | First messages from snippets | Read the ACTUAL first messages. BUT: If user has project context (CLAUDE.md), a short first message like "fix the auth bug" IS clear — project context fills the gap. Don't penalize brevity when context already exists. Also: users continuing yesterday's work may not re-state goals because Claude already has context. | | 📐 Scope Discipline | scope analysis, task shifts detected | Tightened detection: "also add error handling" within same task = NOT a scope shift. Only true topic changes count (e.g., auth work → suddenly asking about CSS). "next" as continuation ≠ scope shift. Read the scope_analysis data carefully. |

Grading Scale:

S (90-100): Exceptional. Top-tier. Rare.

A (75-89): Strong. Clearly effective.

B (60-74): Solid. Gets the job done, room to grow.

C (40-59): Developing. Noticeable gaps.

D (0-39): Needs attention. Significant room for improvement.

3.2 — Assign Archetypes

Based on your analysis, assign:

Primary Archetype — the dominant pattern:

🎯 The Sniper — precise prompts, few iterations, high clarity. Surgical.

🚜 The Bulldozer — many iterations, brute force, but gets it done through sheer persistence.

🧪 The Scientist — tests, verifies, debates. Treats AI output as hypothesis.

🏃 The Sprinter — fast sessions, quick tasks, high throughput. Values speed.

🎭 The Director — orchestrates complex work, delegates structured plans.

🧘 The Philosopher — deep discussions, rich context, thinks WITH the AI.

⚡ The Hacker — rapid-fire exec-heavy, move fast, terminal warrior.

🎨 The Architect — structured, plans before executing, methodical.

🔥 The Phoenix — resilient. Recovers from failures, adapts strategy mid-session.

🌀 The Explorer — still finding their style, experimenting.

Secondary Archetype — the supporting pattern.

Shadow Archetype — the pattern that emerges under stress/frustration. Read the frustration moments in the snippets:

When frustrated, does the user become a Bulldozer (repeat same prompt)?

Do they become passive (just "ok" everything)?

Do they become directive and short-tempered?

Do they pivot and show Phoenix behavior?

Do they give up (abandon session)?

Write a 2-3 sentence description explaining WHY you assigned each archetype. Use specific evidence from the sessions.

3.3 — Behavioral Patterns (The Psychology Part)

Read the session snippets carefully. Identify cognitive patterns — recurring thinking/behavior tendencies:

Look for these common developer-AI cognitive patterns:

"Mind Reader Expectation" — assuming Claude has context it doesn't

"Scope Creep Tendency" — sessions that balloon beyond original intent

"Premature Optimization" — optimizing before things work

"All-or-Nothing Restart" — restarting from scratch instead of iterating

"Context Amnesia" — not providing project context at session start

"Test Later Syndrome" — only requesting tests after bugs appear

"Delegation Without Review" — accepting Claude's output without checking

"Prompt Recycling" — rephrasing same thing instead of changing approach

"Emotional Escalation" — frustration building across a session

"Happy Path Blindness" — not considering edge cases

Identify 3-5 patterns you ACTUALLY see in the data. Don't make them up. Quote brief examples if possible.

Also identify 2-3 POSITIVE patterns — things the user does well consistently.

NEW positive patterns to look for:

"Context Recovery" — user restarts session to get fresh context. This is SMART, not abandonment. If you see short sessions followed by same-project restarts, call this out as a positive.

"Token Conscious" — short replies to checkpoints ("continue", "yes proceed") = user is optimizing token spend. If informed_checkpoint count is high, this is deliberate efficiency.

"Progressive Delegation" — user starts directive, then trusts Claude more as session progresses. Look at session journey: if start phase has long detailed prompts and end phase has shorter trusting messages with more tool usage = growing trust.

NEW mild-concern patterns to look for:

"Premature Closure Request" — user asks Claude to commit/push before verifying the work. Speed over safety. Not critical but worth noting gently.

3.4 — Session Journey Map

From the session_journeys data, describe the user's typical session arc:

How do sessions START? (energy, clarity, length)

How does the MIDDLE feel? (productive iteration vs spinning?)

How do sessions END? (clean wrap-up vs fadeout vs abandonment?)

Represent this visually using text:

Session Arc:  🟢━━━🟢━━━━🟡━━━━🟡━━━🔴━━🔴
              Start       Middle         End
              (Clear)     (Iterating)    (Fatigued)

3.5 — Chrono Analysis

From the chrono_analysis data:

When is the user most active?

When are they most EFFICIENT (fewer turns per session)?

Best/worst day of the week?

Visualize with simple bars:

🌅 Morning:   ████████░░ (strong)
☀️ Afternoon: █████████░ (PEAK)
🌙 Evening:   ████░░░░░░ (declining)
🦉 Night:     ██░░░░░░░░ (rare)

3.6 — Communication DNA

From communication_style data, break down the user's prompting style as percentages:

Directive:     ████████░░ 42%  — "Do X, then Y"
Collaborative: ██████░░░░ 28%  — "What if we..."
Exploratory:   ███░░░░░░░ 18%  — "How does X work?"
Passive:       ██░░░░░░░░ 12%  — "ok / proceed"

3.7 — Growth Tips

Based on the WEAKEST 3 parameters, write 2-3 specific, actionable tips. These MUST be:

Tied to actual patterns you observed

Specific enough to act on THIS WEEK

Framed as growth opportunities, not criticisms

Include a brief example from their sessions if possible

3.8 — Fun Stats

Pull interesting numbers:

Total messages, tokens, time spent

Peak coding hour and day

Longest/chattiest session

Most used tools

Project distribution

Any surprising or funny stats

Phase 4: Generate Report

Write the full report as a markdown file. Save it to:

~/.claude/skills/introspect/reports/introspect-DD-MM-YYYY_HH-MM-SS.md

Report Structure:

# 🔍 Introspect Report
> Your Developer DNA — [Date]
📋 Scan Details
[date range, sessions analyzed, projects covered, totals]
🏆 Overall Grade: [X] ([score]/100)
[visual bar]
📊 Parameter Scorecard
[table with ALL 13 params — grade, score, and YOUR interpretation]
🧬 Dreyfus Skill Ladder
The JSON contains dreyfus_and_tiers with per-parameter Dreyfus levels (Novice → Advanced Beginner → Competent → Proficient → Expert).
Present this as a visual skill ladder for EACH parameter:

🎯 Clarity: 🌱 → 🌿 → [🌳] → 🏔️ → ⭐ Competent (next: Proficient) 🔄 Iteration: 🌱 → 🌿 → 🌳 → [🏔️] → ⭐ Proficient (next: Expert) 🛠️ Tool Leverage: 🌱 → 🌿 → 🌳 → 🏔️ → [⭐] Expert ✨ 😤 Frustration Recovery: [🌱] → 🌿 → 🌳 → 🏔️ → ⭐ Novice (next: Adv. Beginner) ...

Highlight:
Strongest param (highest Dreyfus level) → celebrate it
Weakest param (lowest Dreyfus level) → this is the growth priority
Overall Dreyfus level from the data
Dreyfus levels reference (from the JSON):
| Level | Grade Range | Description |
|-------|------------|-------------|
| 🌱 Novice | D (0-39) | Follows rules rigidly, needs step-by-step guidance |
| 🌿 Advanced Beginner | C (40-59) | Recognizes patterns, still needs structure |
| 🌳 Competent | B (60-74) | Plans, prioritizes, handles complexity |
| 🏔️ Proficient | A (75-89) | Sees the big picture, intuitive decisions |
| ⭐ Expert | S (90-100) | Fluid, automatic, creates reusable patterns |
🎭 Your Archetype
Primary: [Archetype]
[2-3 sentences with evidence]
Secondary: [Archetype]
[1-2 sentences]
🌑 Shadow (Under Stress): [Archetype]
[2-3 sentences about stress behavior — THIS is the psychology]
🏆 Archetype Tier Placement
Show where the user's primary archetype sits in the tier ranking:

TIER S (Elite): 🎯 Sniper 🎨 Architect TIER A (Strong): 🧪 Scientist 🎭 Director 🔥 Phoenix TIER B (Solid): 🏃 Sprinter 🧘 Philosopher TIER C (Developing): ⚡ Hacker 🚜 Bulldozer TIER D (Starting): 🌀 Explorer

← YOU ARE HERE: [Tier X] — [Archetype Name]

The archetype_tiers data in the JSON has the full tier mapping.
⚠️ IMPORTANT: Avoid confusion between Archetype Tier and Overall Grade!
These are TWO DIFFERENT things:
Archetype Tier = which STYLE of developer you are (Director = Tier A style)
Overall Grade = how well you EXECUTE across all parameters (could be B even with A-tier archetype)
Example: "You're a Director (A-tier archetype) — great style for complex work. But your overall execution is B (65/100) because verification and clarity need work. The archetype is WHO you are, the grade is HOW WELL you're doing it."
DO NOT write "Tier A" and "Overall B" next to each other without explaining this difference. Users WILL be confused. Always clarify:
"Your archetype (Director) sits in Tier A — that's a strong collaboration style"
"Your overall score is B (65) — that's how effectively you're using that style across all params"
"Think of it like: you're driving an A-tier car, but your driving skill is B. The car is great — sharpen the driving."
🚀 How to Level Up — Your Evolution Path
THIS IS THE MOST IMPORTANT SECTION. 75-85% personalized, 15-25% generic framework.
The JSON contains evolution_paths with generic tips per archetype. Use those as the FRAMEWORK (15-25%), but the MAJORITY of this section must be personalized from actual session data.
Structure:
#### Current → Target

[Current Archetype] (Tier X) → [Target Archetype] (Tier Y)

#### Generic Evolution Roadmap (15-25%)
Pull from evolution_paths in the JSON. Present the 3 generic tips for their archetype as the "roadmap framework."
#### Personalized Level-Up Tips (75-85%)
THIS IS WHERE YOU EARN YOUR PAY. Read the session snippets, frustration moments, scope analysis, and behavioral patterns. Write 3-5 SPECIFIC tips that are:
Evidence-based — cite specific session behavior you observed
  - "In session abc123, you retried the same prompt 4 times before changing approach. Next time, change strategy after retry #2."
Actionable THIS WEEK — not vague advice, concrete behavior changes
  - "Before your next TELEPORT session, write a 1-line scope statement as your first message: 'This session: [specific goal] only'"
Tied to the archetype transition — each tip should move them TOWARD the target archetype
  - "Directors become Architects by adding structure. Try ending your next 3 sessions with: 'Done: X. Next: Y.'"
Include a "micro-challenge" — one tiny habit to try
  - "🎯 Micro-challenge: For the next 5 sessions, set a 'scope alarm' — if you say 'also' or 'one more thing' more than twice, split the session."
#### What Leveling Up Looks Like
Describe concretely what changes when they reach the next tier:
Fewer turns per task
Higher completion rate
Less frustration
More first-prompt resolution
Specific metrics that would improve
🧬 Behavioral Patterns
Patterns Detected:
[3-5 cognitive patterns with brief evidence/examples]
What You Do Well:
[2-3 positive patterns]
📈 Session Journey Map
[typical session arc visualization + interpretation]
⏰ Chrono Analysis
[time block bars + peak/worst analysis]
🗣️ Communication DNA
[style breakdown with percentages + interpretation]
📐 Scope & Focus
[context switching score + scope discipline findings]
Completion Breakdown (new categories):
The JSON now provides smarter completion detection:
✅ Completed — clear wrap-up signals (thanks, done, ship it)
⏸️ Paused — natural stop (end of day, low frustration, decent session length)
🔄 Continued — same project session starts shortly after (user just split work)
❌ Abandoned — high frustration mid-task, very short, no follow-up
Use healthy_rate (completed + paused + continued) instead of raw completion_rate for the Momentum score. true_abandon_rate is the real concern metric.
Present this breakdown in the report — it's much fairer than the old binary "completed vs abandoned."
🌱 Growth Areas
[2-3 specific, actionable tips tied to actual data — these should COMPLEMENT the Level Up section, not duplicate it. Focus on the weakest 2-3 PARAMETERS here, while Level Up focuses on ARCHETYPE evolution.]
🎲 Fun Stats
[interesting numbers, project distribution, tools, etc.]
🔑 Key Takeaway
[One personalized paragraph — insightful, motivating, human. End with their evolution path: "You're a [Current] heading toward [Target]. One change: [specific tip]."]

*Generated by Introspect 🔍 — Run again in a week to track your growth!*

Tone Guidelines

Professional but fun — like a cool coach, not a corporate HR review

Growth-oriented — frame everything as "here's how to level up", never "you suck at this"

Evidence-based — always tie insights to actual session data

Light humor welcome — "You asked Claude the same thing 5 times... persistence is a virtue? 😅"

Psychologically rich — the behavioral patterns section should feel genuinely insightful

Honest — don't sugarcoat D-grades, but deliver them with care

Personal — this should feel like it was written FOR this specific developer, not from a template

Important Constraints

Read-only — NEVER modify any Claude session data

Local only — nothing is sent externally

Privacy — don't include actual code or sensitive content in the report

No judgment on content — we analyze HOW they work, not WHAT they build

No spelling/grammar judgment — we're analyzing workflow, not writing