nci-manipulation-analysis

What this skill does

# 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](references/categories.md))
3. Calculate composite factors and overall score (see [references/scoring.md](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](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](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):**

```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 |
| Historica