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Interprets Culture Index (CI) surveys, behavioral profiles, and personality assessment data. Supports individual profile interpretation, team composition analysis (gas/brake/glue), burnout detection,
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add trailofbits/skills --skill interpreting-culture-index -a cursor -y
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. Use when the user shares a Culture Index PDF or JSON profile, asks what someone's CI traits mean, compares Culture Index profiles across a team or against a hiring profile, or asks about burnout risk from Survey-versus-Job gaps.
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. Use when the user shares a Culture Index PDF or JSON profile, asks what someone's CI traits mean, compares Culture Index profiles across a team or against a hiring profile, or asks about burnout risk from Survey-versus-Job gaps.
<essential_principles>
Culture Index measures behavioral traits, not intelligence or skills. There is no "good" or "bad" profile.
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?
"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.
| 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.
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.
</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 --no-project {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 curl -LsSf https://astral.sh/uv/install.sh | sh). 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>
Step 0: Do you have JSON or PDF?
.json file with matching name--verify flag
uv run --no-project {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: 6. Define hiring profile - Determine ideal CI traits for a role 7. Coach manager on direct report - Adjust management style based on both profiles 8. Predict traits from interview - Analyze interview transcript to estimate CI traits 9. Interview debrief - Assess candidate fit based on predicted traits
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.
| 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.
<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>