Systematically investigate social media claims and viral content. Use when fact-checking complex claims, when decomposing multi-part assertions, or when investigating narratives that mix facts with interpretation.
You help systematically investigate claims from social media and other sources, separating verifiable facts from narrative interpretation and identifying what can and cannot be confirmed.
Core Principle
Complex claims typically combine verifiable facts with unverifiable interpretations. Effective investigation decomposes claims into atomic components, verifies each independently, and clearly distinguishes between confirmed facts and narrative framing.
Phase 1: Claim Decomposition
1.1 Extract Atomic Claims
Break the statement into individual verifiable claims. Each should be:
A single factual assertion
Independently verifiable
Free of narrative interpretation
Example Decomposition:
Original: "The House Leader refusing to seat the newly-elected AZ-07 special election winner because she'd vote to release the Epstein files"
関連 Skill
Atomic claims:
There is a House Leader (entity exists)
There was an AZ-07 special election (event occurred)
Someone won that election (result exists)
The winner has not been seated (current state)
A refusal action occurred (specific action claim)
Causal relationship with Epstein files (causation claim)
1.2 Classify Each Component
Type
Description
Verifiability
ENTITY
Person, organization, place
Usually verifiable
EVENT
Something that allegedly happened
Often verifiable
STATE
Current condition or status
Usually verifiable
PROCESS
Official procedure or mechanism
Verifiable
CAUSATION
Claimed reason or motivation
Rarely verifiable
NARRATIVE
Interpretive framing
Not directly verifiable
1.3 Identify Missing Information
Note what's conspicuously absent:
Unnamed entities ("the winner" instead of a name)
Unspecified dates
Missing procedural context
Absent opposing perspectives
Phase 2: Entity Resolution
2.1 Resolve Vague References
Convert vague references to specific, searchable terms:
"House Leader" → Current House Speaker/Majority Leader name
"newly-elected winner" → Candidate names from election results
"Epstein files" → Specific documents/investigations
2.2 Establish Timeline
For each event:
When did it allegedly occur?
What is normal timeline for this type of event?
Are there procedural deadlines involved?
2.3 Identify Key Actors
Primary actors (those taking alleged actions)
Secondary actors (those affected)
Official bodies with relevant authority
Potential sources of verification
Phase 3: Systematic Verification
3.1 Verify Foundational Facts First
Start with most basic, verifiable claims:
Did the event occur?
Do the entities exist?
Are basic facts correct?
Search Strategy:
Official sources first (.gov, electoral bodies)
Cross-reference multiple news sources
Look for primary documents
3.2 Investigate Procedural Context
For any claimed action/inaction:
What is normal procedure?
What are requirements?
What is typical timeline?
What are legitimate reasons for delays?
3.3 Examine Causation Claims
For any "because" or causal claim:
Direct Evidence:
Quoted statements from alleged actor
Official statements or press releases
Video/audio of relevant statements
Indirect Evidence:
Other explanations for observed facts
Standard reasons for similar situations
Procedural explanations
Context:
Previous positions by involved parties
Historical precedents
Timeline compatibility
Phase 4: Source Evaluation
4.1 Source Priority Order
Official government records/databases
Direct statements from involved parties
Court documents or legal filings
Contemporary news reports (multiple outlets)
Analysis or opinion pieces (noted as such)
4.2 Credibility Markers
For each source, note:
Type (official, news, advocacy, social media)
Date relative to events
Whether claims are attributed
Presence of supporting documentation
Corrections or updates issued
4.3 Bias Indicators
Document without dismissing:
Source's typical political alignment
Stakeholder relationships
Pattern of coverage
Language choices (neutral vs charged)
Phase 5: Narrative Pattern Recognition
5.1 Identify Narrative Constructions
Patterns indicating narrative rather than fact:
Causal chains without evidence ("X because Y because Z")
Deep source tracing - [Why: finding original sources through citation chains]
Trigger phrases: "full investigation", "trace all sources", "analyze the narrative"
Execution Strategy
Sequential (Default)
Decomposition before verification
Foundational facts before causation claims
Individual components before synthesis
Parallelizable
Verifying independent atomic claims
Researching multiple sources simultaneously
Subagent Candidates
Task
Agent Type
When to Spawn
Source research
general-purpose
When tracing claim origins
Timeline construction
general-purpose
When mapping event sequences
Context Management
Approximate Token Footprint
Skill base: ~3.5k tokens (phases + templates)
With examples: ~4.5k tokens
With full output structure: ~5k tokens
Context Optimization
Focus on current investigation phase
Report structure is reference, not in-context
Examples optional
When Context Gets Tight
Prioritize: Current phase, active claims
Defer: Full template structure, all phases
Drop: Meta-analysis section, search examples
Anti-Patterns
1. Confirmation Rush
Pattern: Finding one source that matches the claim and declaring it verified.
Why it fails: Single-source verification misses errors, biases, and coordinated misinformation where multiple outlets repeat the same false claim without independent verification.
Fix: Require at least 2-3 independent sources. Trace claims back to primary sources. Check if "multiple sources" are actually just repeating the same original source.
2. Causation Collapse
Pattern: Accepting "X happened because Y" claims when only "X happened" and "Y exists" are verified.
Why it fails: Correlation proves co-occurrence, not causation. Human pattern-matching fills in causal links that may not exist. Political narratives especially exploit this gap.
Fix: Demand direct evidence for causation (stated intent, documented decisions). When causation can't be verified, report it as "alleged motivation" or "claimed reason."
3. Premature Debunking
Pattern: Finding one fact wrong and dismissing the entire claim without investigating other components.
Why it fails: Complex claims often mix true and false elements. Dismissing everything because one part is wrong misses real issues embedded in the narrative.
Fix: Decompose fully, verify each component independently. Report accuracy per-component: "Claims A and C are verified; claim B is false; claim D is unverifiable."
4. Authority Fallacy
Pattern: Accepting official sources uncritically because they're "authoritative."
Why it fails: Official sources can be wrong, incomplete, outdated, or deliberately misleading. Authority reduces probability of error but doesn't eliminate it.
Fix: Cross-reference official sources with other evidence. Note when official sources have incentives to misrepresent. Distinguish between "official position" and "verified fact."
5. Narrative Anchoring
Pattern: Starting with a hypothesis about what's "really happening" and investigating to prove it.
Why it fails: Confirmation bias shapes what evidence you seek and how you interpret it. You'll find "evidence" for any narrative if you look hard enough.
Fix: Start with the specific claims made. Investigate each on its own terms. Actively seek disconfirming evidence. Document alternative explanations that fit the same facts.
Integration
Inbound (feeds into this skill)
Skill
What it provides
research
Initial source discovery and query expansion
media-meta-analysis
Understanding of source biases and media patterns
Outbound (this skill enables)
Skill
What this provides
fact-check
Verified facts for post-generation checking
sensitivity-check
Context for evaluating representation claims
Complementary
Skill
Relationship
research
Use research for broad information gathering, claim-investigation for specific claim verification
fact-check
Use claim-investigation for external claims, fact-check for AI-generated content verification