interpreting-culture-index

<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? </principle>

"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>

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>

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>

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 {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.

After reading the workflow, follow it exactly.

<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/):

</workflows_index>

<quick_reference>

Trait Colors:

Energy Utilization Formula:

Utilization = (Job EU / Survey EU) × 100

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

Gas/Brake/Glue:

Score Precision:

</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>