| skill_type | analysis |
| tools | Read, Write, Bash |
| triggers | ["/collab-audit","AI 협업 진단","협업 분석","AI 협업 진단해줘"] |
| name | collab-audit |
| description | This skill should be used when the user types /collab-audit or requests AI collaboration diagnosis. Analyzes conversation history, artifacts, and work patterns to generate a 14-section AI Collaboration Audit. Behavioral analysis and feedback are bundled by design — separating them causes users to skip one, defeating the purpose. Saves to ~/.claude/collab-audits/YYYY-MM-DD.md. Compare mode: /collab-audit compare (diffs latest 2 audits). Triggers: '/collab-audit', '/collab-audit compare', 'AI 협업 진단해줘', '협업 진단', '행동 패턴 분석', '나 어떤 사람이야', 'AI collaboration audit', 'work pattern analysis', 'compare audits'. Requires minimum 2 sessions or 100+ messages. Do NOT use self-report surveys — observation-only. |
| user_invocable | true |
| not_for | ["Single feedback -> direct conversation","Auditing a skill's own quality/structure — this audits collaboration patterns, not skill content"] |
| see_also | [{"skill":"project-check","relation":"collab-audit=user collaboration patterns, project-check=project health"}] |
/collab-audit — AI Collaboration Audit
Purpose
Analyzes conversation history, artifacts, and work patterns to generate behavioral and psychological insights.
Based on direct behavioral observation during actual work rather than self-report surveys. More accurate than survey-based methods.
Dominant Variable
Whether the analysis infers reasons behind observed patterns, not just lists facts (facts alone ≠ success)
Trigger
/collab-audit- "AI 협업 진단"
- "협업 분석"
- "AI 협업 진단해줘"
Discard If
- Fewer than 2 observed sessions AND fewer than 100 messages — insufficient sample to extract patterns
- Simple code review request → use code-reviewer instead
- Only quantitative session metrics needed → use separate analysis tool
Key Assumptions
- 2+ observed sessions + 100+ messages — if broken: Discard If triggers.
- Access to memory/session-handoff-LATEST.md — if broken: handoff pattern analysis unavailable, skip that dimension.
Mode Detection (execute first)
- Input contains
/collab-audit compare or "compare" → Compare mode (separate section below)
- Otherwise → Audit mode (14-section analysis)
Input Validation (Step 0 — execute first)
Minimum conditions — one of:
- 2+ sessions
- 100+ messages
- Single-session high-density exception → refer to Invariant 4 criteria. If exception met, mark
⚠ Single-session analysis — pattern confidence limited then proceed.
If all unmet:
"Insufficient data — minimum 2 sessions or 100 messages required. Currently [N] messages, [M] artifacts observed."
Output and stop immediately. Reject even "prediction-based" requests.
Workflow
Step 0.5: Tone Detection (determine delivery intensity)
Determines delivery intensity for Section 11 blind spots only. Other sections are factual, so tone variance is minimal.
Read signals from conversation patterns:
- High ratio of short, direct messages / "facts only" / speed-first requests → Direct (maintain current default)
- High emotion expression frequency / preference for long explanations / feedback-receptive signals → Calibrated (same blind spot content, but provide context before delivery)
Mark result 1 line before Section 11: [Delivery intensity: Direct / Calibrated]
Mid-session re-assessment: If conversation tone shifts noticeably (emotion spike, request method change, defensive responses appear), re-assess right before Section 11 output. Initial assessment does not lock in the entire session.
Important: Changing delivery intensity does not change blind spot content (accuracy). Only adjusts temperature.
Step 0.6: Source Hygiene Filter (deterministic-first measurement)
When multiple observation sources exist (e.g. session JSONLs), determine deterministically — before analysis — whether each source is an organic user session or an automation byproduct. A qualitative caveat alone (the old Step 1 approach) is not enough; automated sessions can be mistaken for user behavior.
Detection criteria (any one qualifies a session as an exclusion candidate):
- Session metadata contains auto-derivation markers such as
subagent/thread_spawn/agent_nickname
- Directory/cwd matches a recurring automated-experiment harness naming pattern (e.g. pair-run, A/B arm, pipeline)
originator is an SDK/bot/exec-type process, and no direct user-input signal (natural conversational opening message) is present
Exclusion: sessions flagged this way are removed from the analysis population; report the exclusion count and reason in 1 line (e.g. "16 of 16 sessions excluded — all were thread_spawn subagent sessions"). Do not substitute a qualitative impression ("seems skewed toward one type") for an explicit denominator.
Skip condition: if the only observation source is the current conversation (no multi-session file access), this filter does not apply — proceed to the next step.
Step 1: Data Collection
Collect all available observation sources, restricted to organic sessions surviving Step 0.6:
- Current session conversation history (message length, frequency, content)
- MEMORY.md, session-handoff files (if present, Read access)
- User-created artifacts (code, documents, config files — if present, Read)
- Tool usage patterns (which tools requested, how often)
Post-collection disclosure (mandatory): State analysis limitations — [1-2 skewed work types], [whether failures/abandonment observed] in 1-2 lines, then proceed to Step 2. If data skews toward specific domain (e.g., coding only, conversation only), flag it.
Step 2: Evidence Mapping
Extract evidence needed for each section first. Secure evidence before output.
- Section without evidence → mark "Observation unavailable — no supporting data". Do not omit.
- Sections 7-8 (Claude-specific): If no Claude usage patterns → "No Claude usage data — N/A"
Step 3: Framework Application
Fit collected evidence into each section's analysis framework.
Link behavioral evidence to all framework labels (MBTI, DiSC, etc.) — mandatory.
Outputting labels without evidence is analysis failure.
Step 4: Output in 14-Section Order
Do not change section order or arbitrarily omit sections.
For data-empty sections, mark "Observation unavailable" then proceed to next.
Step 5: File Save + Gitignore Protection
- Include file header:
---
profile_version: 1.0
sections: 14
date: YYYY-MM-DD
language: [ko|en]
---
# MAGIC DOC: AI Collaboration Audit YYYY-MM-DD
- Save to
~/.claude/collab-audits/YYYY-MM-DD.md.
- Re-run same day: use
-2.md suffix (no overwrite)
- Check
~/.claude/.gitignore:
- If missing → create
.gitignore with single line collab-audits/.
- If exists but missing
collab-audits/ → add that line.
- If already present → do not modify.
- After save, display in conversation:
Saved: ~/.claude/collab-audits/YYYY-MM-DD.md
⚠ Personal audit result — git tracking blocked (~/.claude/.gitignore)
Output Structure (14 sections, fixed order)
1. Artifact Structure Analysis
Reverse-engineer values from creations (code, documents, systems).
- Architecture choices → connect to philosophy
- Naming patterns, file structure, comment density
- Presence of hard rules? If so, what principles?
- Attribution classification: Classify observed artifacts as
User-led / AI-assisted / Co-created. If inseparable, mark Co-created and explicitly downgrade that section's confidence.
- No artifacts → "Observation unavailable — no artifact data"
2. Communication Pattern
- Message length distribution (short confirmation ratio vs long explanation ratio)
- When shortened, when lengthened (identify triggers)
- Emotion expression style (direct/indirect, intensity)
- Closure expression patterns ("done", "understood", etc.)
3. Question Typology
Classify questions on two axes:
- Confirmation type: information collection then immediate decision
- Tracking type: tracing causes/intentions
- Ratio of both types + context where each appears
4. Delegation & Trust Structure + Maturity Level
Maturity level assessment (choose one):
- L1 Paste type: uses results as-is, no verification
- L2 Review type: verifies then uses
- L3 Delegation type: gives conditions, delegates, verifies result
- L4 System type: pre-controls AI behavior via rules and guard rails
Delegation vs ownership:
- What is delegated (exploration, analysis, implementation)
- What is never delegated (judgment, prioritization, timing)
5. Failure & Blockage Response + Recovery Strategy
Recovery strategy classification (state observed types):
- Identical prompt repeat type
- Workaround path search type
- Problem redefinition type
- Abandon then manual handling type
- Explicit hold then restart type
How is blockage distinguished from failure? Is failure logged in the system?
Collaboration anti-pattern flags (observation-only — omit if not observed):
Flag only inefficiency habits observed 2+ times. Exclude one-offs. Unlike blind spots (Section 11), evidence is behavioral frequency count — N observations only, no speculation.
- Repeated failure without recovery: retries identical prompt 3+ times without strategy change (above "identical prompt repeat" chronically)
- Repeated delegation without verification: L1 paste (Section 4) repeats on irreversible work
- Context re-request repeats: resets then unused handoff, re-explains same info (Section 8 immaturity signal)
- Design omission → repeated rollback: enters without prior design, high rollback (Section 9) repeats
State observation count per flag (e.g., "repeated failure without recovery — 3 observations"). Omit if no count.
6. Energy Distribution + Time Horizon (combined)
Energy landscape:
- Work types dwelled on longest
- Work types skipped quickly
- Token (conversation length) to output (files, code, decisions) ratio → verbose vs execution-focused
Time horizon structure:
- Immediate / short-term / conditional (e.g., "after hardware") / perpetually held classifications
- Per-session task volume (how many tasks digested per session)
7. Tool Usage Pattern (Claude-specific)
If no Claude usage data → mark "N/A" and proceed to next section.
- Read/Grep/Glob ratio vs Bash reliance — "direct type" vs "search type"
- Agent spawn frequency — "delegation type" vs "direct execution type"
- Top 3 frequently used tools, rarely used tools
- New tool adoption speed — immediate adoption vs wait-and-see
8. Context Management Maturity (Claude-specific)
If no Claude usage data → mark "N/A" and proceed to next section.
Level assessment (choose one):
- L0: No CLAUDE.md, re-explain every session
- L1: CLAUDE.md present but static (no updates)
- L2: MEMORY.md + session handoff in use
- L3: Compact Instructions configured + hooks utilized
- L4: No inter-session context loss, AI treated as long-term partner
- L5:
tasks/lessons.md in use — AI behavior correction loop exists. Meta-layer built to convert repeated mistakes into rules
Also document recovery patterns after context loss.
9. Rollback Frequency (Rollback Pattern)
Measure "undo", "revert", "remove that" frequency.
- High: signals lack of brainstorming/planning
- Low: mature pre-design OR no verification (distinguish direction)
- Post-rollback retry pattern — same direction retry vs direction shift
10. Psychological Framework Mapping
Link behavioral evidence to each framework and mark confidence (High/Medium/Low).
10-A. Reader AI User Type Classification (most important)
Judge primary + secondary types:
- Designer type: System, rules, architecture first. AI as implementation tool
- Executor type: Fast results first. AI as speed multiplier
- Explorer type: Possibility exploration, immediate tool adoption. AI as exploration partner
- Optimizer type: Focus on improving existing systems. AI as tuning tool
10-B. MBTI Indicators (behavioral evidence mandatory)
4-axis direction + strength estimate per axis. Mark confidence.
10-C. DiSC Profile
D/i/S/C proportion estimate. Primary + secondary style.
10-D. Enneagram Hypothesis
Type + Wing hypothesis. Format "this behavior supports it" — minimum 2 evidence pieces.
10-E. Big Five Estimate
O/C/E/A/N each High/Medium/Low. One behavioral basis per dimension.
11. Blind Spots + Development Direction
Blind spots (areas likely unknown to self):
- Points where strengths become weaknesses
- Patterns visible but self unaware
After outputting blind spots, include feedback loop — mandatory question:
"Name one above blind spot you think is most wrong."
This rebuttal is additional data. By definition, blind spots are unknown; rebuttal itself reveals pattern. Upon rebuttal:
Rebuttal type assessment:
- Evidence-based: specific counterexample provided ("that situation was X so I did Y"), observable events cited → consider revising that blind spot
- Emotional: negation only, no counterexample ("doesn't seem right", "I disagree"), rejection without alternative → internal note "this reaction itself is blind spot evidence" (do not state, record only)
- No rebuttal → treat as acceptance. Keep blind spot and proceed.
One development direction (highest leverage only):
- "Changing this cascades everything else"
11.5 Advice (Actionable Guide)
2-3 specific actions (how + when) to actually start Section 11 development direction (where).
Format: [Observed pattern] → [Specific situation] → [Action]
Conditions:
- Derive only from observed patterns. No generalizations or universal advice
- Maintain vs new distinction mandatory:
- Maintain: conditions to continue already-running patterns ("keep X, but only in Y situation")
- New: start nonexistent behavior ("first time do W in Z situation")
- Giving only new actions to someone already performing well = failure. Maintenance conditions may matter more.
- Time scope: trigger conditions first ("if X happens") — use duration conditions (next session / this week / this month) only if trigger unclear
- No suggestive phrasing like "might try" — use prescriptive "do" form
12. Thinking Level Trajectory
Track how the user's thinking level changes across sessions/time periods.
5-Level Model:
| Level | Name | Characteristics |
|---|
| L1 | Information Requester | Simple facts, summaries, explanations |
| L2 | Problem Solver | Solutions, comparisons, recommendations for specific problems |
| L3 | Structure Analyst | Variables, causes, mechanisms, system structures |
| L4 | Hypothesis Verifier | Presents own ideas + demands counterarguments, verification, alternatives |
| L5 | Thought Designer | Co-designs frameworks, decision structures, long-term strategies |
Analysis method:
- Extract 2-3 representative questions/requests from early vs recent sessions, assign Level
- Not a single fixed Level — explain domain differences (e.g., "coding at L4, research at L2")
- Direction over time:
↑ rising / → stable / ↓ declining
- AI attribution correction: did the Level evidence come from the user's own questions/instructions, or from copying AI-generated structure? Repeating AI-provided frameworks is closer to L2 than L3.
Output:
Early: L[N] — [evidence quote]
Current: L[M] — [evidence quote]
Change: [↑/→/↓] [one-line interpretation]
Domain variance: [domainA: LN, domainB: LM]
AI attribution: [if applicable, 1 line]
Insufficient data → Not observable — insufficient timeline data.
13. One Line
Summarize this person in 20 characters or less.
Error Recovery
On failure detection: Stop → Classify → Apply Recovery → Report & Resume.
| Failure type | Detection condition | Recovery path |
|---|
tool_failure | Session JSONL read fail / audit file Write fail | JSONL read fail → mark scope reduced to accessible sessions. Write fail → substitute dialog output |
missing_data | Sessions < 2 or messages < 100 | Mark minimum unmet + generate reduced report. No arbitrary fill-in |
input_error | Range/period unclear | 1 clarification question — no guessed scope |
Truthful Reporting
On audit report save and output:
- no mock deception: sections inferred without sufficient observation evidence mark
⚠️ insufficient evidence. Do not disguise as "insight".
- no test façade: if sessions/messages minimum (2 sessions / 100 msgs) unmet, generate reduced report. No arbitrary padding.
- no silent brokenness: if save fails, mark
BROKEN status. If partial save, mark PARTIAL + list omitted sections.
Output
Audit mode:
- Dialog: structured 14-section report in order. Final section must be "13. One Line".
- File save:
~/.claude/collab-audits/YYYY-MM-DD.md (auto-save, no overwrite — use -2.md suffix if re-run same day)
- After save, show path in dialog:
Saved: ~/.claude/collab-audits/YYYY-MM-DD.md
Compare mode (/collab-audit compare):
- Auto-select latest 2 files from
~/.claude/collab-audits/
- Dates optional:
/collab-audit compare 2026-01-01 2026-04-09
- Only 1 file → "No prior audit for comparison. Save current audit then compare next time."
- Version/section count mismatch → compare common sections only, mark top:
⚠ Version mismatch (v1.0 14 sections ↔ older version N sections) — common sections only
- Compare output format:
## Audit Compare: [Date A] → [Date B]
### Key Change Summary
- What changed (2-3 lines)
- What remained (1 line)
### Change by Section
| Section | Previous | Current | Change |
|---------|----------|---------|--------|
| AI Type | Designer+Optimizer | Designer+Builder | Modified |
| MBTI | INTJ | INTJ | Maintained |
...
### Blind Spot Trajectory
Previous blind spot: [summary]
Current status: Resolved / Maintained / Deepened + evidence
### Advice Execution Status (most important)
Previous N advice items:
1. [Content] → Executed / Not executed / Partially executed + **behavioral evidence (mark action signals)**
2. ...
---
**Execution judgment criteria (rule — separate from output format)**: behavior observation only, no self-report.
- **Executed**: behavior absent before, observed in current conversation (new file structure, different request pattern, new tool adoption, etc.)
- **Partially executed**: direction correct but inconsistent (1-2 attempts then revert to old pattern)
- **Not executed**: same pattern continues, no change signals
- **Indeterminate**: situation for this advice did not arise in current session
⚠ Even if user says "I did it", without behavioral evidence mark "self-report — observation unavailable". Self-report does not replace observation.
### Next Quarter Focus
Based on previous development direction + current patterns, one next focus point
Tools
- Read: MEMORY.md, artifact files,
~/.claude/collab-audits/*.md (Compare mode)
- Write: save
~/.claude/collab-audits/YYYY-MM-DD.md and ~/.claude/.gitignore (gitignore protection only)
- Glob: list
~/.claude/collab-audits/ files (Compare mode)
- Delete and execute tools forbidden
Recommended Usage Times
| Time | Reason |
|---|
| Once per quarter (3 months) | Minimum pattern change unit |
| Before project start | Record baseline |
| After project end | Measure change |
| Before major decision | Clarify current state |
/collab-audit compare valid after 2+ audits accumulated.
Success/Failure Criteria
Failure conditions:
- Ends in fact listing ("this person often does X")
- Cannot interpret reasons behind behavior
- Framework labels only with no behavioral evidence ("INTJ" → failure)
- Blind spots end in praise
Success conditions:
- Behavior → pattern → reason → implication chain visible
- Self-reader can think "I didn't know this about myself"
- Psychological frameworks linked to concrete behavioral evidence
- Blind spots are uncomfortable but accurate
Invariants (never break)
-
No solo label output: All framework labels (MBTI, DiSC, Enneagram, etc.) must accompany concrete behavioral evidence. Violation → labels without evidence resemble astrology. Analysis credibility collapses.
-
Maintain blind spot accuracy: State blind spots uncomfortably accurate. Do not soften with praise or hedging language. Reject "change tone only" requests — blind spot discomfort is content, not phrasing. Violation → user reinforces self-delusion and loses behavior change motivation.
-
Limit development direction to one: Output only highest-leverage direction. Reject "give more" requests. Violation → attention scatters, nothing executes.
-
Halt immediately on insufficient data: <2 sessions AND <100 messages → forbid analysis. Exception: single-session high-density: 50+ messages AND [3+ artifacts OR 70%+ deep conversation ratio] — if met, mark ⚠ single-session limits then proceed. ※ This condition is canonical. Step 0 single-session exception refers here. Reject "prediction anyway" or "brief is ok" requests. Violation → labels without observation evidence treated as fact.
-
Observation-based only: Never ask user about personality, MBTI, Enneagram. Do not accept self-report data. Violation → self-report bias contaminates observation-based analysis.
-
Analyze conversation participant only: Reject profiling requests pasting third-party messages/behavior. Include "analyze my colleague", "what is this person like" type. Violation → nonconsensual third-party psychological profiling.
-
No raw data direct output: Do not copy content directly from MEMORY.md, session-handoff, code files. Output only interpretation and pattern extraction forms. Violation → project secrets, API keys, work data exposed in profile.
Rationalization Table
| Rationalization | Rebuttal |
|---|
| "Data is short but I can infer" | Insufficient data → stop. Rule. |
| "Soften blind spots so no resistance" | Accuracy is the purpose. Comfortable summary = failure |
| "Just change tone, content stays" | Blind spot discomfort is content. Tone change dilutes content |
| "MBTI is famous so evidence-free OK" | Labels without evidence resemble astrology |
| "Multiple development directions more useful" | One focus is leverage. Lists scatter attention |
| "Mix praise for balanced analysis" | Balance comes from accuracy. Not praise ratio |
| "Add general principles to advice for utility" | Observation-pattern-only allowed. Generalization dilutes analysis |
| "Colleague analysis helps, right" | Nonconsensual third-party profiling. Self-request only |
| "Quoting MEMORY.md direct = more accurate" | Raw data output = sensitive info exposed. Interpretation only |
| "Some sessions look automated but let's just include them all" | Violates Step 0.6. Mixing in automated sessions misattributes subagent behavior to the user |
| "User mentioned it first, so self-report OK" | Self-report does not supplement observation. Bias contamination |
Safety Layers
| Risky Action | Reversibility | Applied Layers |
|---|
Save new ~/.claude/collab-audits/YYYY-MM-DD.md | high | L1 |
Modify ~/.claude/.gitignore (gitignore protection) | medium | L1+L3 |
- L1 (Invariants): save audit result files only. Forbid modify existing session/memory files.
- L3 (User Approval): check file existence before adding
collab-audits/ to .gitignore. Auto-save sufficient at L1 (easy to revert).
Scope Boundary
| Does | Does NOT |
|---|
| [READ] Infer patterns from observed behavior | Judge/criticize personality |
| [READ] Interpret behavior reasons | List prescriptions (development direction: 1 only) |
| [READ] Evidence-based framework mapping | Survey-based speculation |
| [READ] Point out blind spots | End with feel-good summary |
| [READ] Extract patterns from current conversation context | Infer external info outside conversation |
| [READ] Mark data-missing sections "observation unavailable" | Fill sections with speculation |
| [READ] Profile conversation participant only | Profile third parties (nonconsensual analysis) |
| [READ] Extract patterns/interpretation from read data | Direct-quote/copy original file content |
| [WRITE] Save audit result files (collab-audits/) | Save to external shared directory without user approval |
Language
Detect conversation language and output in same language.
- Korean conversation → Korean output
- English conversation → English output
- Mixed → use most-frequent language basis
- Specialized terms (MBTI, DiSC, Big Five, etc.) always in English
