Claimify

Extract claims from text and map their logical relationships into structured argument networks.

Overview

Claimify transforms messy discourse (conversations, documents, debates, meeting notes) into analyzable claim structures that reveal:

• Explicit and implicit claims
• Logical relationships (supports/opposes/assumes/contradicts)
• Evidence chains
• Argument structure
• Tension points and gaps

Workflow

1. Ingest: Read source material (conversation, document, transcript)
2. Extract: Identify atomic claims (one assertion per claim)
3. Classify: Label claim types (factual/normative/definitional/causal/predictive)
4. Map: Build relationship graph (which claims support/oppose/assume others)
5. Analyze: Identify structure, gaps, contradictions, implicit assumptions
6. Output: Format as requested (table/graph/narrative/JSON)

Claim Extraction Guidelines

Atomic Claims

Each claim should be a single, testable assertion.

Good:

• "AI adoption increases productivity by 15-30%"
• "Psychological safety enables team learning"
• "Current training methods fail to build AI fluency"

Bad (not atomic):

• "AI is useful and everyone should use it" → Split into 2 claims

Claim Types

Type	Definition	Example
Factual	Empirical statement about reality	"Remote work increased 300% since 2020"
Normative	Value judgment or prescription	"Organizations should invest in AI training"
Definitional	Establishes meaning	"AI fluency = ability to shape context and evaluate output"
Causal	X causes Y	"Lack of training causes AI underutilization"
Predictive	Future-oriented	"AI adoption will plateau without culture change"
Assumption	Unstated premise	[implicit] "Humans resist change"

Relationship Types

• Supports: Claim A provides evidence/reasoning for claim B
• Opposes: Claim A undermines or contradicts claim B
• Assumes: Claim A requires claim B to be true (often implicit)
• Refines: Claim A specifies/clarifies claim B
• Contradicts: Claims are mutually exclusive
• Independent: No logical relationship

Output Formats

Table Format (default)

| ID | Claim | Type | Supports | Opposes | Assumes | Evidence |
|----|-------|------|----------|---------|---------|----------|
| C1 | [claim text] | Factual | - | - | C5 | [source/reasoning] |
| C2 | [claim text] | Normative | C1 | C4 | - | [source/reasoning] |

Graph Format

Use Mermaid for visualization:

graph TD
    C1[Claim 1: AI increases productivity]
    C2[Claim 2: Training is insufficient]
    C3[Claim 3: Organizations should invest]
    
    C1 -->|supports| C3
    C2 -->|supports| C3
    C2 -.->|assumes| C4[Implicit: Change requires structure]

Narrative Format

Write as structured prose with clear transitions showing logical flow:

## Core Argument

The author argues that [main claim]. This rests on three supporting claims:

1. [Factual claim] - This is supported by [evidence]
2. [Causal claim] - However, this assumes [implicit assumption]
3. [Normative claim] - This follows if we accept [prior claims]

## Tensions

The argument contains internal tensions:
- Claims C2 and C5 appear contradictory because...
- The causal chain from C3→C7 has a missing premise...

JSON Format

For programmatic processing:

{
  "claims": [
    {
      "id": "C1",
      "text": "AI adoption increases productivity",
      "type": "factual",
      "explicit": true,
      "supports": ["C3"],
      "opposed_by": [],
      "assumes": ["C4"],
      "evidence": "Multiple case studies cited"
    }
  ],
  "relationships": [
    {"from": "C1", "to": "C3", "type": "supports", "strength": "strong"}
  ],
  "meta_analysis": {
    "completeness": "Missing link between C2 and C5",
    "contradictions": ["C4 vs C7"],
    "key_assumptions": ["C4", "C9"]
  }
}

Analysis Depth Levels

Level 1: Surface

• Extract only explicit claims
• Basic support/oppose relationships
• No implicit assumption mining

Level 2: Standard (default)

• Extract explicit claims
• Identify clear logical relationships
• Surface obvious implicit assumptions
• Flag apparent contradictions

Level 3: Deep

• Extract all claims (explicit + implicit)
• Map full logical structure
• Identify hidden assumptions
• Analyze argument completeness
• Red-team reasoning
• Suggest strengthening moves

Best Practices

1. Be charitable: Steelman arguments before critique
2. Distinguish: Separate what's claimed from what's implied
3. Be atomic: One claim per line, no compound assertions
4. Track evidence: Note source/support for each claim
5. Flag uncertainty: Mark inferential leaps
6. Mind the gaps: Identify missing premises explicitly
7. Stay neutral: Describe structure before evaluating strength

Common Patterns

Argument Chains

Premise 1 (factual) → Premise 2 (causal) → Conclusion (normative)

Implicit Assumptions

Often found by asking: "What must be true for this conclusion to follow?"

Contradictions

Watch for:

• Same speaker, different times
• Different speakers, same topic
• Explicit vs implicit claims

Weak Links

• Unsupported factual claims
• Causal claims without mechanism
• Normative leaps (is → ought)
• Definitional ambiguity

Examples

See references/examples.md for detailed worked examples.