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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

scripts/culture_index/models.py

"""Data models for Culture Index profile extraction."""

from __future__ import annotations

from dataclasses import dataclass, field


@dataclass
class ExtractionResult:
    """Result of processing a single PDF."""

    pdf_name: str
    success: bool
    output_path: str | None = None
    error: str | None = None
    warnings: list[str] = field(default_factory=list)

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

Security

What our scanner noticed· Medium risk

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  • Downloads piped into a shell
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Attribution and licence

Published by
Trail of Bits
Publisher
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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