interpreting-culture-index
Uses survey data on behavioral traits and personality profiles to assist with hiring, team building, and coaching.
Install
mkdir -p .claude/skills/interpreting-culture-index && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3025" && unzip -o skill.zip -d .claude/skills/interpreting-culture-index && rm skill.zipInstalls to .claude/skills/interpreting-culture-index
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Interpret Culture Index survey results
- →Analyze team composition using gas/brake/glue
- →Detect burnout risk via Survey vs Job comparison
- →Predict behavioral traits from interview transcripts
- →Mediate team conflict using profile data
How it works
It analyzes behavioral traits based on distance from the population mean (red arrow) rather than absolute values, using specific workflows to compare survey and job graphs.
Inputs & outputs
When to use interpreting-culture-index
- →Assessing team composition
- →Detecting employee burnout
- →Conflict mediation
About this skill
<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:
- Check if JSON already exists (same directory as PDF, or ask user)
- If not, run extraction with verification:
uv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json] - Visually confirm the verification summary matches the PDF
- 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?
- If JSON provided or found: Use it directly (skip extraction)
- Check same directory as PDF for
.jsonfile with matching name - Check if user provided JSON path
- Check same directory as PDF for
- If only PDF: Run extraction script with
--verifyflaguv run {baseDir}/scripts/extract_pdf.py --verify /path/to/file.pdf [output.json] - 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:
- Interpret an individual profile - Understand one person's traits, strengths, and challenges
- Analyze team composition - Assess gas/brake/glue balance, identify gaps
- Detect burnout signals - Compare Survey vs Job, flag stress/frustration
- Compare multiple profiles - Understand compatibility, collaboration dynamics
- 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:
- Did you use relative positions? Never stated "A is 8" without context
- Did you reference the arrow? All trait interpretations relative to arrow
- Did you compare Survey vs Job? Identified any behavior modification
- Did you avoid value judgments? No traits called "good" or "bad"
- 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-soci
Content truncated.
When not to use it
- →Non-CI behavioral assessments like DISC or Myers-Briggs
- →Clinical psychological diagnosis
- →Sole basis for hiring or firing decisions
Prerequisites
Limitations
- →Requires extracted JSON or PDF data
- →Visual estimation of PDF values is prohibited
How it compares
It uses a relative distance-based interpretation method that accounts for adaptive behavior modification, unlike standard personality tests.
Compared to similar skills
interpreting-culture-index side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| interpreting-culture-index (this skill) | 1 | 2mo | Review | Intermediate |
| market-sizing-analysis | 73 | 2mo | No flags | Intermediate |
| exploratory-data-analysis | 15 | 2mo | Review | Intermediate |
| model-compare | 7 | 7mo | Review | Advanced |
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