/interpreting-culture-index

name: interpreting-culture-index
description: 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.
allowed-tools: Bash Read Grep Glob Write

<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.**

| 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>

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 C