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Interpreting Culture Index

Trail of BitsOfficialPublished by Trail of Bits

  • Skill
  • Role or persona
  • Business and productivity
  • CC-BY-SA-4.0
  • Medium risk
  • Has scripts

From the author

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.
— the description by Trail of Bits

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured methodology for analyzing Culture Index (CI) survey data. It emphasizes the importance of relative trait positioning over absolute scores, the distinction between hardwired survey traits and adaptive job behaviors, and the identification of burnout risks through energy utilization analysis. The skill guides users through interpreting individual profiles, assessing team composition using the Gas/Brake/Glue framework, and coaching managers based on behavioral patterns.

What it does

  • Interprets individual behavioral profiles and identifies archetypes.
  • Analyzes team composition to balance Gas, Brake, and Glue traits.
  • Detects burnout risk by comparing survey traits against job-adaptive behaviors.
  • Predicts behavioral traits from interview transcripts.
  • Provides coaching recommendations for managers based on direct report profiles.
  • Extracts and verifies CI data from PDF documents into structured JSON.

When to use it

  • When you need to interpret an individual's Culture Index survey results.
  • When assessing team dynamics or identifying gaps in team composition.
  • When a team member shows signs of burnout or frustration in their role.
  • When defining ideal behavioral profiles for new hiring roles.
  • When mediating conflict between team members with different behavioral styles.
  • When preparing for 1:1 coaching sessions with direct reports.

Advantages

  • Prevents common interpretation errors like comparing absolute values.
  • Provides a clear, data-driven approach to identifying burnout and flight risk.
  • Standardizes the process of extracting and analyzing behavioral data.
  • Offers actionable management and communication strategies for different archetypes.
  • Reduces subjectivity in hiring and team development decisions.

Usage examples

“I have a Culture Index PDF for a candidate. How do I interpret their profile?”
The agent will guide you through the extraction process using the provided script, then analyze the resulting JSON to identify the candidate's archetype and key behavioral strengths.
“Our team feels stuck. Can you analyze our composition?”
The agent will request the CI profiles for the team members, assess the balance of Gas, Brake, and Glue traits, and identify potential gaps or areas of friction.
“How do I manage a direct report who is a 'Craftsman' archetype?”
The agent will provide specific management strategies, communication preferences, and motivational needs tailored to the Craftsman pattern.
“Is this employee at risk of burnout?”
The agent will compare the employee's survey traits with their job-adaptive behaviors to calculate energy utilization and identify stress signals.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/trailofbits/interpreting-culture-index/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip interpreting-culture-index.zip -d .agents/skills

Documentation by Trail of Bits

<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.

  • 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.**

DistanceLabelPercentileInterpretation
On arrowNormative50thFlexible, situational
±1 centileTendency~67thEasier to modify
±2 centilesPronounced~84thNoticeable difference
±4+ centilesExtreme~98thHardwired, 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>

When to Use

  • Interpreting Culture Index survey results (individual or team)

  • Analyzing CI profiles from PDF or JSON data

  • Assessing team composition using Gas/Brake/Glue framework

  • Detecting burnout risk by comparing Survey vs Job graphs

  • Defining hiring profiles based on CI trait patterns

  • Coaching managers on how to work with specific CI profiles

  • Predicting CI traits from interview transcripts

  • Mediating team conflict using CI profile data

When NOT to Use

  • For non-CI behavioral assessments (DISC, Myers-Briggs, StrengthsFinder, Predictive Index, Enneagram)

  • For clinical psychological assessments or diagnoses

  • As the sole basis for hiring/firing decisions — CI is one data point among many

<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 --no-project {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 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>

<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 flag

    uv run --no-project {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>

ResponseWorkflow
"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

Archetype 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 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/):

FilePurpose
extract-from-pdf.mdExtract profile data from Culture Index PDF to JSON format
interpret-individual.mdAnalyze single profile, identify archetype, summarize strengths/challenges
analyze-team.mdAssess team balance (gas/brake/glue), identify gaps, recommend hires
detect-burnout.mdCompare Survey vs Job, calculate EU utilization, flag risk signals
compare-profiles.mdCompare multiple profiles, assess compatibility, collaboration dynamics
define-hiring-profile.mdDefine ideal CI traits for a role, identify acceptable patterns and red flags
coach-manager.mdHelp managers adjust their style for specific direct reports
predict-from-interview.mdAnalyze interview transcripts to predict CI traits before survey
interview-debrief.mdAssess candidate fit using predicted traits from transcript analysis
plan-onboarding.mdDesign first 90 days based on new hire profile and team composition
mediate-conflict.mdUnderstand and address friction between team members using their profiles

</workflows_index>

<quick_reference>

Trait Colors:

TraitColorMeasures
AMaroonAutonomy, initiative, self-confidence
BYellowSocial ability, need for interaction
CBluePace/Patience, urgency level
DGreenConformity, attention to detail
LPurpleLogic, emotional processing
ICyanIngenuity, inventiveness

Energy Utilization Formula:

Utilization = (Job EU / Survey EU) × 100

70-130% = Healthy
>130% = STRESS (burnout risk)
<70% = FRUSTRATION (flight risk)

Gas/Brake/Glue:

RoleTraitFunction
GasHigh AGrowth, risk-taking, driving results
BrakeHigh DQuality control, risk aversion, finishing
GlueHigh BRelationships, morale, culture

Score Precision:

ValuePrecisionExample
Traits (A,B,C,D,L,I)Integer 0-100, 1, 2, ... 10
Arrow positionTenths0.4, 2.2, 3.8
Energy Units (EU)Integer11, 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>

Files

workflows/detect-burnout.md

<required_reading>

Read these reference files before analyzing:

  1. references/primary-traits.md - Understanding trait movement

  2. references/anti-patterns.md - Burnout warning signs

</required_reading>

<process>

Step 1: Load Both Charts

You need both Survey (top) and Job (bottom) graphs for burnout detection.

Record for each chart:

  • Arrow position

  • All trait values (A, B, C, D, L, I)

  • EU value

Step 2: Calculate Energy Utilization

Utilization = (Job EU / Survey EU) × 100
UtilizationStatusInterpretation
70-130%HealthySustainable workload
>130%STRESSOverutilization, burnout risk
<70%FRUSTRATIONUnderutilization, flight risk

Example: Survey EU = 41, Job EU = 54 → 54/41 = 132% → STRESS

Step 3: Compare Arrow Movement

Arrow Direction (Survey → Job)SignalMeaning
Arrow shifts RIGHTSTRESSPushing harder than natural
Arrow shifts LEFTFRUSTRATIONPulling back, disengaging
Arrow position unchangedStableEnvironment matches expectations

Step 4: Analyze Each Trait's Movement

For each trait, compare Survey position to Job position:

TraitSurveyJobMovementSignal
A??+/- ??
B??+/- ??
C??+/- ??
D??+/- ??
L??+/- ??
I??+/- ??

Movement interpretation:

TraitRaisingDropping
ANeeds to drive/lead more (may be self-induced or required)Being held back ("Who/what is blocking you?")
BRole requires more relationship buildingRole isolates them (demotivating if naturally high B)
CMore focus/patience required than comfortableMore urgency/variety required than comfortable
DExpected to be more perfectionist/accountableRole allows more flexibility
LTrying to be more emotional/openCompartmentalizing emotions at work
ITrying to be more inventive (traditional approach not working)Focusing on practical execution

Step 5: Identify Polarizing Shifts

Critical flag: When a dot moves from one side of the norm to the other.

Example: D moves from 2nd percentile (left) to 91st percentile (right)

  • This is drastic behavior modification

  • Almost certainly not sustainable

Document any polarizing shifts:

  • [Trait]: Moved from [side] to [opposite side]

  • Severity: [moderate/severe]

Step 6: Check for "Opposite Pattern" Warning

Imminent flight risk signal: When someone's Job behaviors show the opposite of their Survey traits.

Example: Architect (High A, Low C, Low D) → Socializer (Low A, High B, High C) in job

  • All dots flipped to opposite side

  • This person is in severe stress

  • Something must change or they will leave

Step 7: Identify Stress Sources

Job behaviors reflect perception of what the role requires. Sources:

  1. Their leader - Manager expectations/communication

  2. The work itself - Actual responsibilities

  3. Coworkers - People they work with

Ask: "Why do they perceive they need to behave this way?"

Step 8: Calculate 3-6 Month Risk

If behavior modification continues 3-6+ months, expect:

  • Burnout and stress

  • Disengagement

  • Low morale

  • "Mailing it in" (70% effort when capable of 100%)

Assess: How long has this modification been sustained?

Step 9: Check D Trait Specifically

D movement is the most common stress indicator:

D MovementSignal
Raising D significantlyExpected to be more perfectionist/accountable than natural
D "polarizing" (low to high)Most common source of unsustainable stress

Step 10: Compile Burnout Assessment

## Burnout Risk Assessment: [Name]

### Energy Utilization
- Survey EU: [X]
- Job EU: [X]
- Utilization: [X]%
- Status: [HEALTHY / STRESS / FRUSTRATION]

### Arrow Movement
- Survey arrow: [X]
- Job arrow: [X]
- Direction: [Right=Stress / Left=Frustration / Stable]

### Trait Movement Summary
| Trait | Survey | Job | Change | Concern Level |
|-------|--------|-----|--------|---------------|
| A | | | | Low/Medium/High |
| B | | | | Low/Medium/High |
| C | | | | Low/Medium/High |
| D | | | | Low/Medium/High |

### Polarizing Shifts
- [Any traits that flipped sides]

### Risk Level: [LOW / MODERATE / HIGH / CRITICAL]

### Stress Sources (Likely)
1. [Source 1]
2. [Source 2]

### Recommended Actions
1. [Action 1 - e.g., "Discuss workload with manager"]
2. [Action 2 - e.g., "Resurvey in 3 months"]
3. [Action 3 - e.g., "Consider role adjustment"]

### Timeline
- Current duration estimate: [X months]
- Risk horizon: [When burnout likely if unchanged]

</process>

<stress_types>

>130% (STRESS) has two types:

  1. Good stress (self-induced): "I love this company, I care deeply"

    • Still burnout risk if sustained

    • Health issues possible after 3-6 months

  2. Bad stress (overutilization): Too much work OR work requires too much behavior modification

    • More urgent intervention needed

    • May need workload or role adjustment

<70% (FRUSTRATION):

  • Work doesn't fit their traits

  • May have lots to do, but work doesn't match who they are

  • Not punching eject yet, but getting close

  • Flight risk if not addressed

</stress_types>

<anti_patterns>

Avoid these burnout detection mistakes:

  • Ignoring small EU differences: Even 10-15% over 130% matters

  • Focusing only on EU: Trait movement matters too

  • Dismissing "good stress": Self-induced stress still causes burnout

  • Using stale data: Job behaviors should be resurveyed every 6 months

  • Not asking why: Understand the source before recommending solutions

</anti_patterns>

<success_criteria>

Burnout detection is complete when:

  • [ ] Both Survey and Job data analyzed

  • [ ] EU utilization calculated

  • [ ] Arrow movement identified

  • [ ] All trait movements documented

  • [ ] Polarizing shifts flagged

  • [ ] Risk level assigned (Low/Moderate/High/Critical)

  • [ ] Stress sources identified

  • [ ] Specific recommendations provided

  • [ ] Timeline/horizon assessed

</success_criteria>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs network access, Needs Python

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Published by
Trail of Bits
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Trail of Bits (Official)
Source
trailofbits/skills · plugins/culture-index/skills/interpreting-culture-index · commit e4dd0bb
Licence
Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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