---
name: interpreting-culture-index
description: Use when interpreting Culture Index surveys, CI profiles, behavioral assessments, or personality data. Supports individual interpretation, team composition (gas/brake/glue), burnout detection, profile comparison, hiring profiles, manager coaching, interview transcript analysis for trait prediction, candidate debrief, onboarding planning, and conflict mediation. Handles PDF vision or JSON input.
---

<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>

<input_formats>

**JSON (Use if available)**

If JSON data is already extracted, use it directly:
```python
import json
with open("person_name.json") as f:
    profile = json.load(f)
```

JSON format:
```json
{
  "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:
   ```bash
   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>

<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
   ```bash
   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.**

</intake>

<routing>

| Response | Workflow |
|----------|----------|
| "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

**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/`):

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

</workflows_index>

<quick_reference>

**Trait Colors:**
| Trait | Color | Measures |
|-------|-------|----------|
| A | Maroon | Autonomy, initiative, self-confidence |
| B | Yellow | Social ability, need for interaction |
| C | Blue | Pace/Patience, urgency level |
| D | Green | Conformity, attention to detail |
| L | Purple | Logic, emotional processing |
| I | Cyan | Ingenuity, inventiveness |

**Energy Utilization Formula:**
```
Utilization = (Job EU / Survey EU) × 100

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

**Gas/Brake/Glue:**
| Role | Trait | Function |
|------|-------|----------|
| Gas | High A | Growth, risk-taking, driving results |
| Brake | High D | Quality control, risk aversion, finishing |
| Glue | High B | Relationships, morale, culture |

**Score Precision:**
| Value | Precision | Example |
|-------|-----------|---------|
| Traits (A,B,C,D,L,I) | Integer 0-10 | 0, 1, 2, ... 10 |
| Arrow position | Tenths | 0.4, 2.2, 3.8 |
| Energy Units (EU) | Integer | 11, 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>