Culture Index Interpreter

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.

</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 {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>

<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>