Interprets Culture Index (CI) surveys, behavioral profiles, and personality assessment data. Supports individual profile interpretation, team composition analysis (gas/brake/glue), burnout detection, profile comparison, hiring profiles, manager coaching, interview transcript analysis for trait prediction, candidate debrief, onboarding planning, and conflict mediation. Accepts extracted JSON or PDF input via OpenCV extraction script.
npx skills add https://github.com/trailofbits/skills --skill interpreting-culture-index
<essential_principles>
Culture Index measures behavioral traits, not intelligence or skills. There is no "good" or "bad" profile.
<principle name="never-compare-absolutes">
Never compare absolute trait values between people.
The 0-10 scale is just a ruler. What matters is distance from the red arrow (population mean at 50th percentile). The arrow position varies between surveys based on EU.
Why the arrow moves: Higher EU scores cause the arrow to plot further right; lower EU causes it to plot further left. This does not affect validity—we always measure distance from wherever the arrow lands.
Wrong: "Dan has higher autonomy than Jim because his A is 8 vs 5"
Right: "Dan is +3 centiles from his arrow; Jim is +1 from his arrow"
Always ask: Where is the arrow, and how far is the dot from it?
</principle>
<principle name="survey-vs-job">
Survey = who you ARE. Job = who you're TRYING TO BE.
> "You can't send a duck to Eagle school." Traits are hardwired—you can only modify behaviors temporarily, at the cost of energy.
Large differences between graphs indicate behavior modification, which drains energy and causes burnout if sustained 3-6+ months.
</principle>
<principle name="distance-interpretation">
Distance from arrow determines trait strength.
| Distance | Label | Percentile | Interpretation |
|----------|-------|------------|----------------|
| On arrow | Normative | 50th | Flexible, situational |
| ±1 centile | Tendency | ~67th | Easier to modify |
| ±2 centiles | Pronounced | ~84th | Noticeable difference |
| ±4+ centiles | Extreme | ~98th | Hardwired, compulsive, predictable |
Key insight: Every 2 centiles of distance = 1 standard deviation.
Extreme traits drive extreme results but are harder to modify and less relatable to average people.
</principle>
<principle name="l-and-i-exception">
L (Logic) and I (Ingenuity) use absolute values.
Unlike A, B, C, D, you CAN compare L and I scores directly between people:
Only these two traits break the "no absolute comparison" rule.
</principle>
</essential_principles>
<input_formats>
JSON (Use if available)
If JSON data is already extracted, use it directly:
import json
with open("person_name.json") as f:
profile = json.load(f)
JSON format:
{
"name": "Person Name",
"archetype": "Architect",
"survey": {
"eu": 21,
"arrow": 2.3,
"a": [5, 2.7],
"b": [0, -2.3],
"c": [1, -1.3],
"d": [3, 0.7],
"logic": [5, null],
"ingenuity": [2, null]
},
"job": { "..." : "same structure as survey" },
"analysis": {
"energy_utilization": 148,
"status": "stress"
}
}
Note: Trait values are [absolute, relative_to_arrow] tuples. Use the relative value for interpretation.
Check same directory as PDF for matching .json file, or ask user if they have extracted JSON.
PDF Input (MUST EXTRACT FIRST)
⚠️ NEVER use visual estimation for trait values. Visual estimation has 20-30% error rate.
When given a PDF:
uv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json]
If uv is not installed: Stop and instruct user to install it (brew install uv or pip install uv). Do NOT fall back to vision.
PDF Vision (Reference Only)
Vision may be used ONLY to verify extracted values look reasonable, NOT to extract trait scores.
</input_formats>
<intake>
Step 0: Do you have JSON or PDF?
.json file with matching name--verify flag uv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json]
Step 1: What data do you have?
Step 2: What would you like to do?
Profile Analysis:
Hiring & Candidates:
Team Development:
10. Plan onboarding - Design first 90 days based on new hire and team profiles
11. Mediate conflict - Understand friction between two people using their profiles
Provide the profile data (JSON or PDF) and select an option, or describe what you need.
</intake>
<routing>
| Response | Workflow |
|----------|----------|
| "extract", "parse pdf", "convert pdf", "get json from pdf" | workflows/extract-from-pdf.md |
| 1, "individual", "interpret", "understand", "analyze one", "single profile" | workflows/interpret-individual.md |
| 2, "team", "composition", "gaps", "balance", "gas brake glue" | workflows/analyze-team.md |
| 3, "burnout", "stress", "frustration", "survey vs job", "energy", "flight risk" | workflows/detect-burnout.md |
| 4, "compare", "compatibility", "collaboration", "multiple", "two profiles" | workflows/compare-profiles.md |
| 5, "motivate", "engage", "retain", "communicate" | Read references/motivators.md directly |
| 6, "hire", "hiring profile", "role profile", "recruit", "what profile for" | workflows/define-hiring-profile.md |
| 7, "manage", "coach", "1:1", "direct report", "manager" | workflows/coach-manager.md |
| 8, "transcript", "interview", "predict traits", "guess", "estimate", "recording" | workflows/predict-from-interview.md |
| 9, "debrief", "should we hire", "candidate fit", "proceed", "offer" | workflows/interview-debrief.md |
| 10, "onboard", "new hire", "integrate", "starting", "first 90 days" | workflows/plan-onboarding.md |
| 11, "conflict", "friction", "mediate", "not working together", "clash" | workflows/mediate-conflict.md |
| "conversation starters", "how to talk to", "engage with" | Read references/conversation-starters.md directly |
After reading the workflow, follow it exactly.
</routing>
<verification_loop>
After every interpretation, verify:
Report to user:
</verification_loop>
<reference_index>
Domain Knowledge (in references/):
Primary Traits:
primary-traits.md - A (Autonomy), B (Social), C (Pace), D (Conformity)Secondary Traits:
secondary-traits.md - EU (Energy Units), L (Logic), I (Ingenuity)Patterns:
patterns-archetypes.md - Behavioral patterns, trait combinations, archetypesArchetype Deep Profiles (archetype-*.md):
archetype-administrator.md - The Administrator (High A, High B, Low C, Mid D)archetype-coordinator.md - The Coordinator (Low A, High B, Mid C, Low D)archetype-craftsman.md - The Craftsman (Low A, Low B, High C, High D)archetype-daredevil.md - The Daredevil (High A, Low B, Low C, Low D)archetype-debater.md - The Debater (Mid A, Mid-High B, Low C, High D)archetype-facilitator.md - The Facilitator (Low A, Mid B, Mid C, Low D)archetype-influencer.md - The Influencer (Low A, High B, Low C, Low D)archetype-operator.md - The Operator (Low A, Low B, High C, Mid-High D)archetype-persuader.md - The Persuader (High A, High B, Low C, Low D)archetype-philosopher.md - The Philosopher (Low A, Low B, High C, Low D)archetype-rainmaker.md - The Rainmaker (High A, High B, Low C, Low D)archetype-scholar.md - The Scholar (High A, Low B, Low C, High D)archetype-socializer.md - The Socializer (Low A, High B, Low C, Low D)archetype-specialist.md - The Specialist (Low A, Low B, High C, Mid D)archetype-technical-expert.md - The Technical Expert (Low A, Low B, High C, Low D)archetype-traditionalist.md - The Traditionalist (Low A, Low B, High C, High D)archetype-trailblazer.md - The Trailblazer (High A, Mid B, Mid C, Low D)Application:
motivators.md - How to motivate each trait typeteam-composition.md - Gas, brake, glue frameworkanti-patterns.md - Common interpretation mistakesconversation-starters.md - How to engage each pattern and trait typeinterview-trait-signals.md - Signals for predicting traits from interviews</reference_index>
<workflows_index>
Workflows (in workflows/):
| File | Purpose |
|------|---------|
| extract-from-pdf.md | Extract profile data from Culture Index PDF to JSON format |
| interpret-individual.md | Analyze single profile, identify archetype, summarize strengths/challenges |
| analyze-team.md | Assess team balance (gas/brake/glue), identify gaps, recommend hires |
| detect-burnout.md | Compare Survey vs Job, calculate EU utilization, flag risk signals |
| compare-profiles.md | Compare multiple profiles, assess compatibility, collaboration dynamics |
| define-hiring-profile.md | Define ideal CI traits for a role, identify acceptable patterns and red flags |
| coach-manager.md | Help managers adjust their style for specific direct reports |
| predict-from-interview.md | Analyze interview transcripts to predict CI traits before survey |
| interview-debrief.md | Assess candidate fit using predicted traits from transcript analysis |
| plan-onboarding.md | Design first 90 days based on new hire profile and team composition |
| mediate-conflict.md | Understand and address friction between team members using their profiles |
</workflows_index>
<quick_reference>
Trait Colors:
| Trait | Color | Measures |
|-------|-------|----------|
| A | Maroon | Autonomy, initiative, self-confidence |
| B | Yellow | Social ability, need for interaction |
| C | Blue | Pace/Patience, urgency level |
| D | Green | Conformity, attention to detail |
| L | Purple | Logic, emotional processing |
| I | Cyan | Ingenuity, inventiveness |
Energy Utilization Formula:
Utilization = (Job EU / Survey EU) × 100
70-130% = Healthy
>130% = STRESS (burnout risk)
<70% = FRUSTRATION (flight risk)
Gas/Brake/Glue:
| Role | Trait | Function |
|------|-------|----------|
| Gas | High A | Growth, risk-taking, driving results |
| Brake | High D | Quality control, risk aversion, finishing |
| Glue | High B | Relationships, morale, culture |
Score Precision:
| Value | Precision | Example |
|-------|-----------|---------|
| Traits (A,B,C,D,L,I) | Integer 0-10 | 0, 1, 2, ... 10 |
| Arrow position | Tenths | 0.4, 2.2, 3.8 |
| Energy Units (EU) | Integer | 11, 31, 45 |
</quick_reference>
<success_criteria>
A well-interpreted Culture Index profile:
</success_criteria>
Searches across your Notion workspace, synthesizes findings from multiple pages, and creates comprehensive research documentation saved as new Notion pages. Turns scattered information into structured reports with proper citations and actionable insights.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
【强制】所有技术文档查询必须使用本技能,禁止在主对话中直接使用 mcp__context7-mcp 工具。触发关键词:查询/学习/了解某个库或框架的文档、API用法、配置参数、错误解释、版本差异、代码示例、最佳实践。本技能通过 context7-researcher agent 执行查询,避免大量文档内容污染主对话上下文,保持 token 效率。
Generates rich technical documentation pages with dark-mode Mermaid diagrams, source code citations, and first-principles depth. Use when writing documentation, generating wiki pages, creating technical deep-dives, or documenting specific components or systems.
Maximum-saturation research orchestration: ALWAYS proposes the final materials first (PDF+DOCX default), then parallel explore+librarian swarms across codebase, web, official docs, and OSS repos — max-roster teammode when the harness has it — with live journaling, a recursive EXPAND loop driven by leads workers return in message text, empirical verification by running code, and a cited synthesis with charts/Mermaid/assets behind a mandatory visual-QA gate. ACTIVATES ONLY on an explicit user demand for research — the word 'ulw-research' ('/ulw-research', '$ulw-research'), any 'ulw' research wording, 'ultradebate' or 'hyperdebate' research requests, or an explicit request for research / deep research / an ultra-precise investigation, in any language. Never self-activates for ordinary questions, debugging, or implementation context-gathering. While active it overrides exploration-bounding defaults: exhaustive coverage is the goal.
"Solve competition math problems (IMO, Putnam, USAMO, AIME) with adversarial verification that catches the errors self-verification misses. Activates when asked to 'solve this IMO problem', 'prove this olympiad inequality', 'verify this competition proof', 'find a counterexample', 'is this proof correct', or for any problem with 'IMO', 'Putnam', 'USAMO', 'olympiad', or 'competition math' in it. Uses pure reasoning (no tools) — then a fresh-context adversarial verifier attacks the proof using specific failure patterns, not generic 'check logic'. Outputs calibrated confidence — will say 'no confident solution' rather than bluff. If LaTeX is available, produces a clean PDF after verification passes."
Take trailofbits/interpreting-culture-index from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, brew.
Without those the skill loads but fails at the first command.