introspect

name	introspect
description	Deep self-analysis pipeline: collects session/rule/usage data, analyzes across 5 dimensions (strength-shadow + Analogia grounding), produces HTML report. User-invoked via /introspect.

Introspect

A self-analysis pipeline that collects behavioral context, identifies patterns, and produces actionable insights as an HTML report. The pipeline has 4 phases, each building on the previous.

Pipeline Overview

Phase	What	Mode	Output
1. Collect	Gather data from 4 sources	3 parallel inline Task invocations	Raw findings
2. Analyze	Synthesize into profile	AI + user dialogue	Strengths, costs, conflicts
3. Ground	Map to philosophical frameworks	Optional Analogia	Validated mapping
4. Report	Generate HTML report	Automated	.html file

If the user provides a specific question (e.g., "What are my curses?"), orient the analysis toward answering that question rather than producing a generic profile.

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Phase 1: Data Collection

Launch 3 parallel ad-hoc inline Task(general-purpose) invocations. These are not pre-registered agent files — each is an inline prompt task launched for this pipeline only. Each collects from different sources and returns structured findings. All prompts are English per delegation rules; search keywords in quotes are exempt.

Agent 1: Rules & Configuration

PURPOSE: Extract the user's explicitly stated preferences and constraints from their
Claude Code configuration.

Paths below written `{config_dir}/…` take `{config_dir}` = `CLAUDE_CONFIG_DIR` when set, else `~/.claude`. Resolve it ONCE per invocation with Bash `printf '%s\n' "${CLAUDE_CONFIG_DIR-$HOME/.claude}"` and substitute the absolute result before any Read/Glob/Grep call.

COLLECT:
1. Read {config_dir}/CLAUDE.md — extract all stated preferences, principles, and constraints
2. Read all files in {config_dir}/rules/ — extract each rule with its rationale
3. Read {config_dir}/projects/*/memory/MEMORY.md — extract persistent session memory

RETURN FORMAT:
## Stated Preferences
- [preference]: [source file]

## Constraints & Boundaries
- [constraint]: [source file]

## Rule Inventory
- Total rules: N
- Categories: [list]
- Potential redundancies: [if any rules overlap or subsume others]

## Memory Highlights
- [key patterns from session memory]

Agent 2: Usage Patterns (Quantitative)

PURPOSE: Extract quantitative behavioral patterns from usage data.

Paths below written {config_dir}/… take {config_dir} = CLAUDE_CONFIG_DIR when set, else ~/.claude. Resolve it ONCE per invocation with Bash `printf '%s\n' "${CLAUDE_CONFIG_DIR-$HOME/.claude}"` and substitute the absolute result before any Read/Glob/Grep call.

COLLECT:
1. Read {config_dir}/usage-data/report.html — extract key statistics (messages, tools,
   friction types, outcomes, satisfaction, session types)
2. Sample 5-10 facet files from {config_dir}/usage-data/facets/ — extract
   recurring themes, tool usage patterns, satisfaction signals
3. Sample 5-10 session-meta files from {config_dir}/usage-data/session-meta/ —
   extract session types, durations, goal patterns

RETURN FORMAT:
## Quantitative Profile
- Messages: N across M sessions
- Top tools: [ranked list]
- Top goals: [ranked list]
- Outcome distribution: [fully/mostly/partially/not achieved]

## Behavioral Signals
- Session type distribution: [multi-task, iterative, single, exploration]
- Friction patterns: [top 3 with counts]
- Satisfaction distribution

## Notable Patterns
- [3-5 patterns that stand out from the quantitative data]

Agent 3: Session Behavior (Qualitative)

PURPOSE: Extract qualitative behavioral patterns from recent session transcripts.

Paths below written {config_dir}/… take {config_dir} = CLAUDE_CONFIG_DIR when set, else ~/.claude. Resolve it ONCE per invocation with Bash `printf '%s\n' "${CLAUDE_CONFIG_DIR-$HOME/.claude}"` and substitute the absolute result before any Read/Glob/Grep call.

COLLECT:
1. List session files in {config_dir}/projects/ (find recent, large sessions)
2. Sample 3-5 substantive sessions (>50KB) — use Grep to extract:
   - User correction patterns (interruptions, redirections)
   - Decision-making patterns (how user makes choices)
   - Protocol usage patterns (which epistemic protocols, how often)
   - Communication style (directive, collaborative, exploratory)

NOTE: COLLECT operates on session transcripts and protocol patterns only — cross-reference to user-personal analysis artifacts (curated insight files) is intentionally out of scope; the analysis remains substrate-agnostic and reproducible across users.

RETURN FORMAT:
## Interaction Style
- [3-5 observed patterns with session evidence]

## Decision Patterns
- [how the user approaches decisions, with examples]

## Correction Patterns
- [what the user corrects most often, what triggers corrections]

## Protocol/Tool Preferences
- [which tools/protocols are preferred and when]

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Phase 2: Pattern Analysis

After all 3 agents return, synthesize findings across 5 dimensions.

5-Dimension Framework

Analyze each dimension by cross-referencing data from all 3 agents:

Dimension	What to look for	Sources
Communication Style	Directive vs collaborative, interruption patterns, feedback style	Agent 1 (rules), Agent 3 (sessions)
Technical Preferences	Tool choices, language preferences, architecture patterns	Agent 1 (rules), Agent 2 (usage)
Cognitive/Decision Patterns	Verification depth, risk tolerance, abstraction appetite	Agent 2 (friction), Agent 3 (decisions)
Domain/Context	Primary work areas, domain switching patterns	Agent 2 (goals), Agent 3 (sessions)
Premise	Core values, what drives engagement, what causes frustration	All agents

Strength-Shadow Analysis

For each strength identified, find its structural cost (the "shadow"):

Pattern: [observed strength]
Evidence: [specific data from agents]
Shadow: [structural cost that comes with this strength]
Mechanism: [why the strength produces this specific cost]

The key insight: every strength has a shadow, and the shadows often share a common structure (precision creates new complexity, optimization creates blind spots).

Conflict Surface

Compare descriptive patterns (what the user actually does, from Agent 2/3) against prescriptive rules (what the user says to do, from Agent 1). Surface mismatches:

• Rules that the user's own behavior contradicts
• Patterns not captured by any rule
• Rules that may be redundant or subsumable

Present the analysis as text output (strengths, shadows, conflict surface) and proceed to Phase 3 directly — Phase 1 evidence already establishes pattern existence, so pre-validation is Extension-eligible for relay-eligible findings. End the Phase 2 output with a visible red-line discovery line so the correction pathway is explicit: "If any pattern seems misclassified or rules need adjusting, say so — I'll re-derive from there." Conflict-surface synthesis (rules the user contradicts, redundant rules) involves intent inference (deliberate exception vs drift), so the discovery line is required to surface this constitutive layer without a full pre-validation gate. The user may red-line via free response at any subsequent turn (refine, correct, dismiss a pattern, or add context); when that happens, regenerate affected downstream sections on the next turn.

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Phase 3: Philosophical Grounding

Map identified patterns to philosophical frameworks, giving the user a conceptual vocabulary for understanding their cognitive style.

When to activate

Always offer this phase. If the user accepts, invoke /analogia:ground with the key findings as the grounding target. The Analogia protocol handles mapping validation through user dialogue.

Mapping candidates

Common correspondences to consider (not exhaustive):

Pattern	Candidate frameworks
UU exploration preference	Popper (Critical Rationalism), Peirce (Abduction)
AI delegation strategy	Clark & Chalmers (Extended Mind), Ricardo (Comparative Advantage)
Openness to new problems	Zen Beginner's Mind, Socratic questioning
Verification depth	Aristotelian phronesis, Cartesian doubt
System building tendency	Kantian architectonic, Systems thinking

The Analogia process may refine or reject these candidates. User counter-arguments are often the most valuable part — as when "curse" was reframed as "strategy" through an AI-delegation counter-hypothesis.

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Phase 4: Report Generation

Paths below written {config_dir}/… take {config_dir} = CLAUDE_CONFIG_DIR when set, else ~/.claude. Resolve it ONCE per invocation with Bash printf '%s\n' "${CLAUDE_CONFIG_DIR-$HOME/.claude}" and substitute the absolute result before any Read/Glob/Grep call.

Generate an HTML report and save to {config_dir}/epistemic-cooperative/introspect/.

Design System

Read the existing {config_dir}/usage-data/report.html to extract the CSS design system. Match its visual style: Inter font, #f8fafc background, white cards with #e2e8f0 border. See references/report-guide.md for section templates and component patterns.

Required Sections

1. At a Glance — 4-bullet summary (identity, strengths, costs, recommendations)
2. Philosophical Identity — 2x2 grid of philosophy cards (if Phase 3 completed)
3. Division of Labor — Human-AI role visualization (if applicable)
4. Strengths — Green cards with evidence and source dimension tag
5. Structural Costs — White cards with severity badge and mitigation strategy
6. Attitude Recommendations — Gradient cards, ranked by ROI
7. Practice Matrix — Table mapping situations to principles and actions
8. Extended Mind Health — Green/yellow/red indicators for monitoring metrics
9. Next Steps — Horizon cards with concrete next actions

Output

Save as {config_dir}/epistemic-cooperative/introspect/cognitive-partnership-profile.html (date-stamped variant if previous report exists). After saving, print the file path so the user can open it manually.

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Edge Cases

• First-time run: If report.html missing, use CSS from references/report-guide.md.
• Missing data: Note limitations in the report, focus on available sources.
• Specific question: Orient entire analysis toward the question, not a generic profile.
• Skip grounding: If user declines Phase 3, proceed directly to Phase 4. Philosophical identity section becomes optional in the report.