NCI Manipulation Analysis

This skill uses pattern-based manipulation detection that identifies how content tries to influence the reader, not whether claims are factually true. Manipulation techniques leave fingerprints regardless of underlying accuracy.

Use TodoWrite to track these mandatory steps:

<required> 1. Input Processing (text or URL) 2. Score all 20 categories (1-5 scale each) 3. Calculate 5 composite factors 4. Calculate overall score (0-100) 5. Check deep research triggers 6. Generate perspectives (manipulative + legitimate) 7. Output report </required>

Quick Start

For Text Content

1. Read the content provided by user
2. Apply 20-category analysis (see references/categories.md)
3. Calculate composite factors and overall score (see references/scoring.md)
4. Check deep research triggers - if score > 40 or key categories elevated, verify claims
5. Generate dual perspectives
6. Output report in requested format

For URLs

1. Use WebFetch to retrieve content from URL
2. Extract main article/post text
3. Proceed with text analysis workflow
4. Note source metadata (publication, date, author)
5. If triggers met: Use fact-checker agent to verify key claims

First Principles (Summary)

The NCI Protocol is grounded in these principles (see agents/perspective-generator.md for full version):

1. Evidence over authority - Evaluate patterns in content, not source reputation
2. Steel-man interpretation - Present strongest version of each perspective
3. Atomic decomposition - Break claims into smallest verifiable units
4. Source agnosticism - Apply identical standards regardless of source alignment
5. Bidirectional beneficiary analysis - Ask who benefits if believed AND if dismissed
6. Pattern vs. Intent - Focus on techniques; deep research evidence can inform motives

These principles ensure fair, consistent analysis across all content regardless of political or ideological alignment.

Workflow

Step 1: Input Processing

For direct text, record:

INPUT TYPE: Text
LENGTH: [word count]
CONTEXT PROVIDED: [any user context]

For URLs:

INPUT TYPE: URL
URL: [url]
Fetching content with WebFetch...
EXTRACTED: [article title, publication, date if available]

When processing URLs, also check:

• Publication reputation
• Author credentials (if available)
• Publication date and timeliness

Step 2: Score All 20 Categories

For each category, provide:

CATEGORY #[N]: [Name]
Score: [1-5]
Evidence: [Specific quotes/patterns from content]
Confidence: [LOW/MED/HIGH]

See references/categories.md for detailed category definitions and scoring criteria.

Detection signals to look for:

Signal Type	Examples
Emotional vocabulary	fear, outrage, danger, threat, shocking
Urgency language	immediately, urgent, now, before it's too late
Tribal markers	we/they asymmetry, us vs them, real patriots
Dehumanizing terms	animals, vermin, horde, infestation
Attribution asymmetry	stated/confirmed vs claimed/alleged
Logical fallacies	whataboutism, false equivalence, ad hominem

Step 3: Calculate Composite Factors

See references/scoring.md for weights.

COMPOSITE FACTORS:
─────────────────
Emotional Manipulation: [weighted avg of cat 1-5] → [1-5 scale]
Suspicious Timing:      [weighted avg of cat 6-8] → [1-5 scale]
Uniform Messaging:      [weighted avg of cat 9-11] → [1-5 scale]
Tribal Division:        [weighted avg of cat 12-14] → [1-5 scale]
Missing Information:    [weighted avg of cat 15-20] → [1-5 scale]

Step 4: Calculate Overall Score

OVERALL SCORE = Σ(composite_factor × weight × confidence)

Weights:
- Emotional Manipulation: 25%
- Suspicious Timing: 20%
- Uniform Messaging: 20%
- Tribal Division: 15%
- Missing Information: 20%

Scale 1-5 → 0-100: overall_score = (weighted_avg - 1) × 25

Step 5: Check Deep Research Triggers

After calculating scores, check if deep research is needed for claim verification.

Trigger Conditions (if ANY are met, proceed to verification):

DEEP RESEARCH CHECK:
─────────────────────
Overall NCI Score > 40?        [ ] Yes → Verify key claims
Suspicious Timing > 3?         [ ] Yes → Correlate events, timeline
Authority Issues (Cat 16) > 3? [ ] Yes → Verify credentials
Cherry-Picking (Cat 18) > 3?   [ ] Yes → Find omitted context
Historical Parallels > 2?      [ ] Yes → Research precedent campaigns

TRIGGERS MET: [N] → If > 0, proceed to verification

If Triggers Met:

1. Extract Key Claims: Identify 3-5 most impactful factual assertions

2. Invoke Claim Verifier: Use fact-checker agent or /decipon:verify

3. Apply Deep Research: Use ../deep-research/SKILL.md methodology

4. Track Results:

   CLAIM: [Statement]
   STATUS: [VERIFIED / PARTIALLY VERIFIED / UNVERIFIED / CONTRADICTED]
   SOURCE: [URL]
   CONFIDENCE: [1-100]
   NCI IMPACT: [How this affects scores]
   

5. Adjust Scores If Needed:

   • Verified claims → May reduce Authority Issues, Cherry-Picking scores
   • Contradicted claims → Increase relevant category scores
   • Document adjustments in final report

If No Triggers Met: Proceed directly to Step 6 (Perspective Generation).

Step 6: Generate Dual Perspectives

CRITICAL: Always generate BOTH interpretations.

MANIPULATIVE INTERPRETATION:
This content appears designed to [specific manipulation goal].
Key manipulation techniques detected:
- [Technique 1 with evidence]
- [Technique 2 with evidence]
- [Technique 3 with evidence]
Confidence: [X]%

LEGITIMATE INTERPRETATION:
This content may reflect [genuine intent/concern].
Supporting factors:
- [Factor 1]
- [Factor 2]
- [Factor 3]
Confidence: [Y]%

For perspective generation guidance, leverage the critique framework from the deep-research skill if available.

Step 7: Output Report

Standard Format (Markdown):

# NCI Analysis Report

## Content Summary
[Brief description of analyzed content]

## Overall Score: [0-100] [severity indicator]
Confidence: [X]%

## Composite Factors
| Factor | Score | Confidence |
|--------|-------|------------|
| Emotional Manipulation | [X.X]/5 | [%] |
| Suspicious Timing | [X.X]/5 | [%] |
| Uniform Messaging | [X.X]/5 | [%] |
| Tribal Division | [X.X]/5 | [%] |
| Missing Information | [X.X]/5 | [%] |

## Key Findings
[Top 3-5 manipulation indicators with evidence]

## Claim Verification (if deep research triggered)
| Claim | Status | Confidence | Source |
|-------|--------|------------|--------|
| [Claim 1] | [VERIFIED/etc] | [%] | [URL] |
| [Claim 2] | [Status] | [%] | [URL] |

**Score Adjustment**: [Original] → [Adjusted] ([+/-N] due to verification)

## Perspectives
### If Manipulative
[Manipulative interpretation]

### If Legitimate
[Legitimate interpretation]

## Category Details
[Expandable section with all 20 category scores]

## Methodology
NCI Protocol v1.0 - Pattern-based manipulation detection
Deep Research: [Yes/No] - [N] claims verified

Severity Indicators (NCI Protocol v1.0):

• 0-25: [·] Low manipulation risk
• 26-50: [!] Moderate - some concerning patterns
• 51-75: [!!] High - strong manipulation patterns
• 76-100: [!!!] Severe - overwhelming manipulation signs

Integration with Deep Research

This plugin includes the deep-research skill for fact-checking and claim verification. Reference: ../deep-research/SKILL.md

Automatic Triggers

Deep research is recommended when NCI analysis shows:

Trigger	Threshold	Verification Focus
Overall NCI Score	> 40 (upper Moderate)	Verify key claims
Suspicious Timing	> 3	Correlate events, check timeline
Authority Issues	> 3	Verify credentials, expertise claims
Cherry-Picking	> 3	Find omitted context, full data
Historical Parallels	> 2	Research precedent campaigns

Workflow Integration

NCI + DEEP RESEARCH WORKFLOW:
─────────────────────────────
1. Complete NCI analysis (Steps 1-6)
2. Check trigger conditions
3. If triggered:
   - Extract key factual claims
   - Apply claim-verifier agent
   - Use deep research methodology
   - Update scores based on findings
4. Generate final report with verification status

Using the Claim Verifier

After NCI analysis, invoke the claim-verifier agent:

• See ../agents/claim-verifier.md for verification workflow
• Uses source evaluation from ../deep-research/references/source-evaluation.md
• Applies critique framework from ../deep-research/references/critique-framework.md

Verification Commands

Command	Purpose
/decipon:analyze	Pattern analysis (this skill)
/decipon:verify	Fact-check claims with deep research
/decipon:report	Combined analysis + verification report

Source Evaluation Integration

When assessing sources during NCI analysis, apply confidence scoring:

Source Type	Confidence	NCI Consideration
Official documentation	85-95	Reduces Authority Issues if verified
Government/institutional	75-90	Check for political context
Major news (AP, Reuters)	70-85	Generally reliable baseline
Partisan outlets	40-60	Note bias, affects Tribal Division
Anonymous/undated	10-30	Increases Missing Information

See ../deep-research/references/source-evaluation.md for detailed scoring.

Contradiction Handling

When sources disagree during verification:

1. Note the contradiction explicitly
2. Apply confidence scoring to each source
3. Research additional sources to resolve
4. If unresolved, present both perspectives in report

See ../deep-research/references/critique-framework.md for resolution protocol.

Examples

See references/examples.md for historical case studies including:

• Nayirah Testimony (1990) - Score: 88
• Tobacco Industry Campaign - Score: 82
• Modern examples with full category breakdowns

When to Use

Use NCI Analysis:

• Content claiming urgent action needed
• Viral stories with strong emotional triggers
• Content creating clear us-vs-them dynamics
• Stories suspiciously timed with political events
• Content from unknown or questionable sources

Don't Use:

• Simple factual lookups (use fact-checking)
• Opinion pieces clearly labeled as such
• Personal correspondence
• Fiction/entertainment

References

• references/categories.md - All 20 category definitions with detection signals
• references/scoring.md - Scoring methodology, weights, and quantitative formulas
• references/examples.md - Historical case studies for calibration
• references/vocabulary.md - Detection vocabulary lists
• references/guidance.md - Actionable tips per factor

User Interaction

Use the AskUserQuestion tool at key decision points throughout the NCI analysis workflow:

When to Use

• Input ambiguity: Content type, source, or format unclear
• Score interpretation: Borderline scores requiring next-step guidance
• Deep research triggers: Confirmation before escalating to claim verification
• Perspective balance: High disagreement requiring synthesis direction
• Output preferences: Format, detail level, or focus area selection

Example Invocations

Content type clarification (Step 1):

Content could be satire, opinion, or news
→ Use AskUserQuestion tool:
  Question: "Content type affects analysis interpretation. What is this?"
  Options:
  - "News reporting (analyze as factual claims)"
  - "Opinion/editorial (note subjective framing)"
  - "Satire (analyze technique, not truth claims)"
  - "Unclear - analyze as presented"

Deep research trigger (Step 5):

NCI Score: 52, Authority Issues: 4, Cherry-Picking: 3
→ Use AskUserQuestion tool:
  Question: "Analysis triggers suggest claim verification. Proceed with deep research?"
  Options:
  - "Yes, verify key claims (recommended for score > 40)"
  - "No, pattern analysis is sufficient"
  - "Show me which claims would be verified first"

Perspective disagreement (Step 6):

Manipulative confidence: 68%, Legitimate confidence: 42%
→ Use AskUserQuestion tool:
  Question: "Perspectives diverge by 26 points (High). How to present?"
  Options:
  - "Full synthesis with dominant perspective highlighted"
  - "Present both with equal emphasis"
  - "Focus on what verification would clarify"

Output format (Step 7):

Analysis complete, ready for output
→ Use AskUserQuestion tool:
  Question: "How should I format the NCI report?"
  Options:
  - "Full markdown report with all categories"
  - "Summary with key findings only"
  - "JSON for programmatic use"
  - "Information Nutrition Label visual format"

Benefits of Interactive Analysis

• Clearer user intent: Reduces misinterpretation of ambiguous requests
• Appropriate depth: User controls triage vs deep analysis
• Informed escalation: User decides when to invest in verification
• Transparent trade-offs: Options present clear choices with implications