interpreting-culture-index
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npx skills add trailofbits/skillsWorks across Claude Code, Cursor, Codex, Copilot & Antigravity
The Interpreting Culture Index skill provides a structured framework for analyzing behavioral patterns through the Culture Index methodology. It moves beyond raw scores by centering on the delta between individual trait dots and the population mean (the arrow). This approach distinguishes between hardwired 'Survey' traits—representing natural disposition—and 'Job' behaviors, which reflect adaptive roles. By utilizing precise JSON data, the skill identifies potential burnout markers where energy expenditure exceeds sustainable levels. Unlike personality tests that categorize people, this skill quantifies behavioral distance from the norm. It enables precise assessments of communication styles, decision-making tendencies, and role fit, ensuring analysis remains grounded in the specific, calibrated context of an individual's report rather than anecdotal observations or subjective assumptions.
When to Use This Skill
- •Predicting long-term role sustainability and potential burnout risks
- •Assessing team synergy by mapping relative behavioral traits across a group
- •Identifying behavioral gaps between current job requirements and natural candidate strengths
- •Objectively comparing logic and ingenuity scores to inform strategic decision-making
How to Invoke This Skill
Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:
- “Analyze the burnout risk for this candidate based on their CI profile
- “What is the relative strength of the A trait for this team member?
- “Interpret the energy utilization gap between the survey and job graphs
- “Compare the behavioral traits of these two profiles using centile distance
- “Explain the team's behavioral balance based on the provided JSON data
Pro Tips
- 💡Always prioritize the 'distance from the red arrow' over absolute trait values, as the arrow's position shifts with the EU score, making relative distance the true measure of a trait.
- 💡Differentiate between Survey Traits (hardwired 'who you are') and Job Traits (situational 'who you're trying to be') to understand energy expenditure and potential strain.
- 💡When evaluating candidates or existing team members, use the skill to compare their survey profile against the ideal job profile to assess natural fit and areas requiring conscious effort.
What this skill does
- •Calculates behavioral deviation using relative centile distance from the population arrow
- •Analyzes the energy deficit between hardwired survey traits and adaptive job behaviors
- •Processes JSON-formatted CI profiles to eliminate manual extraction error
- •Differentiates between stable behavioral traits and temporary, energy-draining work adaptations
- •Evaluates organizational team balance through aggregated behavioral distribution data
When not to use it
- ✕Attempting to determine intelligence or skill levels instead of behavioral patterns
- ✕Manually estimating chart values via visual inspection of PDFs
- ✕Comparing absolute scores of A, B, C, or D traits across different individuals
Example workflow
- Input the CI PDF or JSON file into the local environment
- Run the extraction script with verification enabled if using a PDF
- Verify that the extracted values match the source document
- Invoke the interpretation skill to calculate relative distance from the arrow
- Examine the variance between the Survey and Job graphs for burnout signals
Prerequisites
- –A valid Culture Index JSON profile or a readable PDF file
- –The 'uv' tool for executing extraction scripts
Pitfalls & limitations
- !Relying on visual estimation from PDFs leads to high error rates
- !Mistaking absolute trait values for relative ones during analysis
- !Ignoring the specific placement of the population arrow in different survey versions
FAQ
How it compares
This skill prevents the common mistake of subjective interpretation by enforcing mathematical rigor via the CI methodology, specifically by mandating relative (rather than absolute) trait analysis.
📄 Full skill instructions — original source: trailofbits/skills
**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.
- **Top graph (Survey Traits)**: Hardwired by age 12-16. Does not change. Writing with your dominant hand.
- **Bottom graph (Job Behaviors)**: Adaptive behavior at work. Can change. Writing with your non-dominant hand.
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:
- Logic 8 means "High Logic" regardless of arrow position
- Ingenuity 2 means "Low Ingenuity" for anyone
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:
1. Check if JSON already exists (same directory as PDF, or ask user)
2. If not, run extraction with verification:
uv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json]3. Visually confirm the verification summary matches the PDF
4. Use the extracted JSON for interpretation
**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?**
1. **If JSON provided or found:** Use it directly (skip extraction)
- Check same directory as PDF for
.json file with matching name- Check if user provided JSON path
2. **If only PDF:** Run extraction script with
--verify flaguv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json]3. **If extraction fails:** Report error, do NOT fall back to vision
**Step 1: What data do you have?**
- **CI Survey JSON** → Proceed to Step 2
- **CI Survey PDF** → Extract first (Step 0), then proceed to Step 2
- **Interview transcript only** → Go to option 8 (predict traits from interview)
- **No data yet** → "Please provide Culture Index profile (PDF or JSON) or interview transcript"
**Step 2: What would you like to do?**
**Profile Analysis:**
1. **Interpret an individual profile** - Understand one person's traits, strengths, and challenges
2. **Analyze team composition** - Assess gas/brake/glue balance, identify gaps
3. **Detect burnout signals** - Compare Survey vs Job, flag stress/frustration
4. **Compare multiple profiles** - Understand compatibility, collaboration dynamics
5. **Get motivator recommendations** - Learn how to engage and retain someone
**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.**
</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:
1. **Did you use relative positions?** Never stated "A is 8" without context
2. **Did you reference the arrow?** All trait interpretations relative to arrow
3. **Did you compare Survey vs Job?** Identified any behavior modification
4. **Did you avoid value judgments?** No traits called "good" or "bad"
5. **Did you check EU?** Energy utilization calculated if both graphs present
Report to user:
- "Interpretation complete"
- Key findings (2-3 bullet points)
- Recommended actions
</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, archetypes**Application:**
-
motivators.md - How to motivate each trait type-
team-composition.md - Gas, brake, glue framework-
anti-patterns.md - Common interpretation mistakes-
conversation-starters.md - How to engage each pattern and trait type-
interview-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:
- Uses relative positions (distance from arrow), never absolute values alone
- Identifies the archetype/pattern correctly
- Highlights 2-3 key strengths based on leading traits
- Notes 2-3 challenges or development areas
- Compares Survey vs Job if both are available
- Provides actionable recommendations
- Avoids value judgments ("good"/"bad")
- Acknowledges Culture Index is one data point, not a complete picture
</success_criteria>
How to Use This Skill Unit
Option A: Project-Specific (Recommended)
- Click "Download" above
- In your project, create the directory:
.agent/skills/interpreting-culture-index/ - Save the file as
SKILL.md - The agent will automatically discover the skill based on its description.
Option B: Global Installation (All Agents)
Save the file to these locations to make it available across all projects:
- Claude Code:
~/.claude/skills/trailofbits/skills/interpreting-culture-index/SKILL.md - Cursor:
~/.cursor/skills/trailofbits/skills/interpreting-culture-index/SKILL.md - Antigravity:
~/.gemini/antigravity/skills/trailofbits/skills/interpreting-culture-index/SKILL.md
🚀 Install with CLI:npx skills add trailofbits/skills
