Interpreting Culture Index
Trail of BitsOfficialPublished by Trail of Bits
- Skill
- Role or persona
- Business and productivity
- CC-BY-SA-4.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured methodology for analyzing Culture Index (CI) survey data. It emphasizes the importance of relative trait positioning over absolute scores, the distinction between hardwired survey traits and adaptive job behaviors, and the identification of burnout risks through energy utilization analysis. The skill guides users through interpreting individual profiles, assessing team composition using the Gas/Brake/Glue framework, and coaching managers based on behavioral patterns.
What it does
- Interprets individual behavioral profiles and identifies archetypes.
- Analyzes team composition to balance Gas, Brake, and Glue traits.
- Detects burnout risk by comparing survey traits against job-adaptive behaviors.
- Predicts behavioral traits from interview transcripts.
- Provides coaching recommendations for managers based on direct report profiles.
- Extracts and verifies CI data from PDF documents into structured JSON.
When to use it
- When you need to interpret an individual's Culture Index survey results.
- When assessing team dynamics or identifying gaps in team composition.
- When a team member shows signs of burnout or frustration in their role.
- When defining ideal behavioral profiles for new hiring roles.
- When mediating conflict between team members with different behavioral styles.
- When preparing for 1:1 coaching sessions with direct reports.
Advantages
- Prevents common interpretation errors like comparing absolute values.
- Provides a clear, data-driven approach to identifying burnout and flight risk.
- Standardizes the process of extracting and analyzing behavioral data.
- Offers actionable management and communication strategies for different archetypes.
- Reduces subjectivity in hiring and team development decisions.
Usage examples
- “I have a Culture Index PDF for a candidate. How do I interpret their profile?”
- The agent will guide you through the extraction process using the provided script, then analyze the resulting JSON to identify the candidate's archetype and key behavioral strengths.
- “Our team feels stuck. Can you analyze our composition?”
- The agent will request the CI profiles for the team members, assess the balance of Gas, Brake, and Glue traits, and identify potential gaps or areas of friction.
- “How do I manage a direct report who is a 'Craftsman' archetype?”
- The agent will provide specific management strategies, communication preferences, and motivational needs tailored to the Craftsman pattern.
- “Is this employee at risk of burnout?”
- The agent will compare the employee's survey traits with their job-adaptive behaviors to calculate energy utilization and identify stress signals.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/trailofbits/interpreting-culture-index/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip interpreting-culture-index.zip -d .agents/skills
Documentation by Trail of Bits
<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 --no-project {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 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?
If JSON provided or found: Use it directly (skip extraction)
Check same directory as PDF for
.jsonfile with matching nameCheck if user provided JSON path
If only PDF: Run extraction script with
--verifyflaguv run --no-project {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-socializer.md- The Socializer (Low A, High B, Low C, Low D)archetype-specialist.md- The Specialist (Low A, Low B, High C, Mid D)archetype-technical-expert.md- The Technical Expert (Low A, Low B, High C, Low D)archetype-traditionalist.md- The Traditionalist (Low A, Low B, High C, High D)archetype-trailblazer.md- The Trailblazer (High A, Mid B, Mid C, Low D)
Application:
motivators.md- How to motivate each trait typeteam-composition.md- Gas, brake, glue frameworkanti-patterns.md- Common interpretation mistakesconversation-starters.md- How to engage each pattern and trait typeinterview-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>
Files
- ATTRIBUTION.mdAdded by AutoGineer959 B
- LICENSELicence file added by AutoGineer19.7 KiB
- SKILL.md14.0 KiB
- agents/openai.yaml246 B
- assets/trail-of-bits-mark.svg3.0 KiB
- references/anti-patterns.md8.1 KiB
- references/archetype-administrator.md3.7 KiB
- references/archetype-coordinator.md3.9 KiB
- references/archetype-craftsman.md3.6 KiB
- references/archetype-daredevil.md3.3 KiB
- references/archetype-debater.md4.1 KiB
- references/archetype-facilitator.md3.7 KiB
- references/archetype-influencer.md2.9 KiB
- references/archetype-operator.md3.6 KiB
- references/archetype-persuader.md3.4 KiB
- references/archetype-philosopher.md3.6 KiB
- references/archetype-rainmaker.md2.6 KiB
- references/archetype-scholar.md4.1 KiB
- references/archetype-socializer.md3.3 KiB
- references/archetype-specialist.md4.1 KiB
- references/archetype-technical-expert.md3.7 KiB
- references/archetype-traditionalist.md3.7 KiB
- references/archetype-trailblazer.md4.0 KiB
- references/conversation-starters.md9.6 KiB
- references/interview-trait-signals.md8.1 KiB
- references/motivators.md6.6 KiB
- references/patterns-archetypes.md6.7 KiB
- references/primary-traits.md12.5 KiB
- references/secondary-traits.md8.3 KiB
- references/team-composition.md6.2 KiB
- scripts/check_deps.pyScript2.6 KiB
- scripts/culture_index/__init__.pyScript564 B
- scripts/culture_index/constants.pyScript3.6 KiB
- scripts/culture_index/extract.pyScript5.6 KiB
- scripts/culture_index/models.pyScript371 B
- scripts/culture_index/opencv_extractor.pyScript15.6 KiB
- scripts/extract_pdf.pyScript7.0 KiB
- scripts/pyproject.toml422 B
- scripts/uv.lock66.6 KiB
- templates/burnout-report.md3.2 KiB
- templates/comparison-report.md3.3 KiB
- templates/hiring-profile.md3.9 KiB
- templates/individual-report.md2.5 KiB
- templates/predicted-profile.md5.6 KiB
- templates/team-report.md3.1 KiB
- workflows/analyze-team.md5.1 KiB
- workflows/coach-manager.md8.8 KiB
- workflows/compare-profiles.md6.2 KiB
- workflows/define-hiring-profile.md7.6 KiB
- workflows/detect-burnout.md6.1 KiB
- workflows/extract-from-pdf.md3.9 KiB
- workflows/interpret-individual.md5.7 KiB
- workflows/interview-debrief.md7.5 KiB
- workflows/mediate-conflict.md9.9 KiB
- workflows/plan-onboarding.md9.2 KiB
- workflows/predict-from-interview.md8.0 KiB
workflows/predict-from-interview.md
<required_reading>
Read these reference files before analyzing:
references/interview-trait-signals.md- Behavioral signals for each traitreferences/primary-traits.md- A, B, C, D trait detailsreferences/secondary-traits.md- L, I trait detailsreferences/patterns-archetypes.md- Pattern identification
</required_reading>
<purpose>
Predict Culture Index traits from interview transcripts. This workflow is used when:
Candidates have been interviewed but haven't taken the CI survey yet
You want preliminary trait estimates before extending an offer
You want to compare predicted vs actual CI (after offer is signed)
Important: This produces predictions, not diagnoses. The actual CI survey will be administered after an offer is signed and before the start date.
</purpose>
<process>
Step 1: Load the Transcript
Request the interview transcript. Ideal format includes:
Interviewer questions clearly marked
Candidate responses clearly marked
Timestamps or durations (helpful but not required)
Multiple interviews if available (more data = higher confidence)
Step 2: Initial Read-Through
First pass - get overall impression:
How does the candidate communicate?
What's their energy level?
What topics engage them most?
What's their default communication style?
Note your initial gut sense before detailed analysis.
Step 3: Analyze A (Autonomy) Signals
Search transcript for:
| Look For | High A | Low A |
|---|---|---|
| Pronouns | "I decided", "I built" | "We decided", "Our team" |
| Credit | Takes personal credit | Deflects to team |
| Questions | Reframes, pushes back | Asks for clarification |
| Initiative | Acted without being asked | Waited for direction |
| Tone | Assertive, confident | Tentative, collaborative |
Record:
Position: High / Low / Normative
Confidence: High / Medium / Low
Key quotes (2-3 examples)
Step 4: Analyze B (Social) Signals
Search transcript for:
| Look For | High B | Low B |
|---|---|---|
| Rapport | Builds connection, asks about interviewer | Gets straight to business |
| Stories | People-centric narratives | Task-centric descriptions |
| Responses | Verbose, talks through thinking | Brief, direct answers |
| Energy | Animated, expressive | Reserved, measured |
| Culture questions | Asks about team, social activities | Asks about work, tools |
Record:
Position: High / Low / Normative
Confidence: High / Medium / Low
Key quotes (2-3 examples)
Step 5: Analyze C (Pace) Signals
Search transcript for:
| Look For | High C | Low C |
|---|---|---|
| Response speed | Pauses, thinks before answering | Rapid responses |
| Structure | Methodical, sequential | Topic-jumps, tangents |
| Ambiguity | Asks for clarification | Comfortable with unknowns |
| Change | Prefers stability | Thrives with pivots |
| Detail | One topic at a time | Multi-threads |
Record:
Position: High / Low / Normative
Confidence: High / Medium / Low
Key quotes (2-3 examples)
Step 6: Analyze D (Conformity) Signals
Search transcript for:
| Look For | High D | Low D |
|---|---|---|
| Precision | Specific numbers, dates | Approximations, ranges |
| Process | References rules, best practices | Describes creative approaches |
| Answers | Structured, follows question format | Free-flowing, interpretive |
| Quality | Mentions checking work, standards | Mentions outcomes, results |
| Flexibility | Follows structure | Challenges premises |
Record:
Position: High / Low / Normative
Confidence: High / Medium / Low
Key quotes (2-3 examples)
Step 7: Analyze L (Logic) - Absolute Scale
Search transcript for:
| Look For | High L (8-10) | Low L (0-2) |
|---|---|---|
| Framing | Data-driven, analytical | Values-driven, emotional |
| Language | "The numbers showed..." | "It felt right..." |
| Difficult topics | Emotion-neutral | Empathetic, emotional |
| Decision-making | Evidence-based | Intuition-based |
Record:
Score estimate: 0-10
Confidence: High / Medium / Low
Key quotes (1-2 examples)
Step 8: Analyze I (Ingenuity) - Absolute Scale
Search transcript for:
| Look For | High I (7-10) | Low I (0-2) |
|---|---|---|
| Problem-solving | Novel approaches | Proven methods |
| Assumptions | Questions, challenges | Accepts, follows |
| Examples | Original, creative | Standard, textbook |
| Routine | Mentions boredom | Describes comfort |
Record:
Score estimate: 0-10
Confidence: High / Medium / Low
Key quotes (1-2 examples)
Step 9: Identify Pattern
Based on trait positions, identify likely pattern:
Cross-reference with references/patterns-archetypes.md:
| If you see... | Likely pattern |
|---|---|
| High A, Low B, Low C, Low D | Architect/Visionary |
| High A, High B, Low C | Rainmaker/Persuader |
| Low A, Low B, High C, High D | Scholar/Specialist |
| Low A, High B, High C | Accommodator |
| Low A, Low B, Low C, High D | Technical Expert |
Only identify pattern if confidence is sufficient - if traits are unclear, note "insufficient data for pattern identification."
Step 10: Flag Uncertainty Areas
Document where evidence is weak:
Traits with only 1-2 data points
Traits that showed inconsistent signals
Topics that weren't covered in interview
Signs of "interview mode" performance
Step 11: Generate Predicted Profile
Output using this structure:
## Predicted Culture Index Profile: [Candidate Name]
**Analysis Date:** [Date]
**Transcript Source:** [Interview type, duration, interviewers]
**Overall Confidence:** [High/Medium/Low]
### Trait Predictions
| Trait | Predicted | Confidence | Evidence |
|-------|-----------|------------|----------|
| A (Autonomy) | [High/Low/Norm] | [H/M/L] | "[Quote]" |
| B (Social) | [High/Low/Norm] | [H/M/L] | "[Quote]" |
| C (Pace) | [High/Low/Norm] | [H/M/L] | "[Quote]" |
| D (Conformity) | [High/Low/Norm] | [H/M/L] | "[Quote]" |
| L (Logic) | [0-10] | [H/M/L] | "[Quote]" |
| I (Ingenuity) | [0-10] | [H/M/L] | "[Quote]" |
### Predicted Pattern
**[Pattern Name]** (if identifiable)
[1-2 sentence description of what this pattern means]
### Strongest Signals
1. [Most clear trait signal with quote]
2. [Second clearest signal with quote]
### Uncertainty Areas
- [Trait/area where more data needed]
- [Trait/area where signals were mixed]
### Interview Context Notes
- [Any factors that may have affected behavior]
- [Signs of interview performance mode]
### Caveats
- This is a prediction based on interview behavior, not a CI survey result
- Interview stress may affect natural behavior patterns
- Actual CI survey will be administered after offer acceptance
- Use for preliminary assessment only - do not treat as definitive</process>
<verification>
Before finalizing prediction:
Did I cite specific quotes? Every trait prediction needs evidence
Did I note confidence levels? Every trait needs H/M/L confidence
Did I flag uncertainties? Where is evidence weak?
Did I include caveats? Predictions are not diagnoses
Did I avoid over-confidence? Especially for low-data traits
</verification>
<anti_patterns>
Avoid these prediction mistakes:
Over-interpreting single quotes: One example isn't a pattern
Ignoring interview context: Stress affects behavior
Treating predictions as definitive: This is hypothesis, not diagnosis
Skipping low-confidence traits: Better to say "uncertain" than guess
Assuming consistency: Interview behavior may differ from daily behavior
Forgetting to cite evidence: Every claim needs a quote
</anti_patterns>
<success_criteria>
Transcript analysis is complete when:
[ ] All 6 traits analyzed with position/score estimates
[ ] Each trait has confidence level (H/M/L)
[ ] Each trait has supporting quotes from transcript
[ ] Pattern identified (if sufficient confidence)
[ ] Uncertainty areas documented
[ ] Caveats clearly stated
[ ] Output follows standard format
</success_criteria>
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs network access, Needs Python
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- Trail of Bits
- Publisher
- Trail of Bits (Official)
- Source
- trailofbits/skills · plugins/culture-index/skills/interpreting-culture-index · commit e4dd0bb
- Licence
- Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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