claim-investigation▌
jwynia/agent-skills · updated Apr 8, 2026
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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.
Claim Investigation: Systematic Fact-Checking Skill
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"
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")
- Mind-reading claims ("thinks that," "wants to")
- Selective fact inclusion
- Temporal conflation (mixing time periods)
- False dichotomies
5.2 Find Counter-Narratives
For each narrative:
- What facts support it?
- What facts complicate it?
- What alternative narratives explain same facts?
- What facts are excluded?
5.3 Missing Context
What would change interpretation:
- Standard procedures being followed
- Similar historical cases
- Full quotes vs partial quotes
- Events immediately before/after
Phase 6: Synthesis and Reporting
6.1 Report Structure
VERIFIED FACTS:
- [Fact] (Source: [citation])
DISPUTED/UNCLEAR:
- [Claim]:
- Supporting: [source]
- Contradicting: [source]
- Unable to verify: [what's missing]
CONTEXT NEEDED:
- [Procedural context]
- [Historical precedent]
- [Timeline considerations]
NARRATIVE ELEMENTS:
- [Claim]
- Facts that support: [list]
- Facts that complicate: [list]
- Alternative explanations: [list]
6.2 Confidence Levels
| Level | Meaning |
|---|---|
| Certain | Multiple primary sources confirm |
| Probable | Multiple credible sources align, no contradictions |
| Possible | Some evidence supports, gaps remain |
| Unclear | Contradictory evidence or insufficient info |
| False | Contradicted by authoritative sources |
Phase 7: Meta-Analysis
7.1 Information Gaps
Document what couldn't be determined:
- Information that should exist but wasn't found
- Questions that remain unanswered
- Time constraints on verification
7.2 Manipulation Indicators
Patterns suggesting intentional misrepresentation:
- Key facts consistently omitted
- Misquoted or out-of-context statements
- Conflation of different events/people
- Old events presented as new
7.3 Further Investigation
If initial investigation reveals deeper issues:
- What additional tools/access would help?
- What questions should be asked of officials?
- What documents should be requested?
Search Query Construction
- Start broad, then narrow
- Use multiple phrasings for same concept
- Include date ranges when relevant
- Search for both supporting and contradicting evidence
- Use exact phrases for quotes, broad terms for concepts
Output Principles
- Lead with verified facts
- Clearly separate facts from analysis
- Include all relevant context
- Present multiple valid interpretations where applicable
- Never assert causation without evidence
- Acknowledge investigation limitations
Output Persistence
Output Discovery
- Check for
context/output-config.mdin the project - If found, look for this skill's entry
- If not found, ask user: "Where should I save investigation reports?"
- Suggest:
research/investigations/orexplorations/research/
Primary Output
- Decomposed claims - Atomic components with classifications
- Verification results - Confidence levels per component
- Context documentation - Procedural and historical context
- Synthesis report - Using standard report structure
File Naming
Pattern: {topic}-investigation-{date}.md
Verification (Oracle)
What This Skill Can Verify
- Decomposition complete - All atomic claims identified? (High confidence)
- Entity resolution - Vague references resolved? (High confidence)
- Source evaluation - Credibility markers documented? (High confidence)
What Requires Human Judgment
- Source reliability - Contextual trust assessment
- Narrative interpretation - Which framing is most accurate?
- Manipulation detection - Intent behind information gaps
Oracle Limitations
- Cannot assess motivations behind claims
- Cannot predict how information will evolve
Feedback Loop
Session Persistence
- Output location: See
context/output-config.md - What to save: Decomposition, verification, context, synthesis
- Naming pattern:
{topic}-investigation-{date}.md
Cross-Session Learning
- Check for prior investigations on related topics
- Build on previous source evaluations
- Failed verifications inform methodology
Design Constraints
This Skill Assumes
- A specific claim to investigate (not general research)
- Verifiable components exist within the claim
- Sources are accessible for verification
This Skill Does Not Handle
- General research - Route to: research
- AI output verification - Route to: fact-check
- Media pattern analysis - Route to: media-meta-analysis
Degradation Signals
- Single-source verification (confirmation rush)
- Accepting causation without evidence
- Dismissing entire claims for single errors
Reasoning Requirements
Standard Reasoning
- Single claim decomposition
- Basic entity resolution
- Simple source evaluation
Extended Reasoning (ultrathink)
- Multi-claim investigation - [Why: claims interact and context builds]
- Narrative analysis - [Why: detecting manipulation patterns]
- 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 |
How to use claim-investigation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add claim-investigation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches claim-investigation from GitHub repository jwynia/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate claim-investigation. Access the skill through slash commands (e.g., /claim-investigation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★55 reviews- ★★★★★Shikha Mishra· Dec 16, 2024
I recommend claim-investigation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aisha Ramirez· Dec 8, 2024
claim-investigation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Evelyn Gonzalez· Dec 4, 2024
claim-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Fatima Wang· Dec 4, 2024
Registry listing for claim-investigation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arjun Perez· Dec 4, 2024
claim-investigation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Layla Farah· Nov 27, 2024
We added claim-investigation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Liam Chen· Nov 23, 2024
Solid pick for teams standardizing on skills: claim-investigation is focused, and the summary matches what you get after install.
- ★★★★★Yash Thakker· Nov 7, 2024
claim-investigation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aisha Flores· Nov 3, 2024
Useful defaults in claim-investigation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Oct 26, 2024
claim-investigation has been reliable in day-to-day use. Documentation quality is above average for community skills.
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