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.
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. 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? 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. 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. 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> <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> Step 0: Do you have JSON or PDF? 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 If only PDF: Run extraction script with --verify flag uv 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
Define hiring profile
Coach manager on direct report
Predict traits from interview
Interview debrief
Plan onboarding
Mediate conflict
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>