# Verifying Claims > Verifies claims and assesses source credibility. Use when fact-checking claims, verifying accuracy, validating sources, detecting hallucinations, triangulating evidence, or performing SIFT or RADAR assessment. - **Type:** Skill - **Install:** `agentstack add skill-isvlasov-rageatc-oss-verifying-claims` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [isvlasov](https://agentstack.voostack.com/s/isvlasov) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [isvlasov](https://github.com/isvlasov) - **Source:** https://github.com/isvlasov/rageatc-oss/tree/main/plugins/rageatc-core-oss/skills/verifying-claims ## Install ```sh agentstack add skill-isvlasov-rageatc-oss-verifying-claims ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Verifying Claims ## Purpose Systematically verify factual claims in both AI-generated and human-generated content using established journalism standards, academic source evaluation frameworks, and AI-specific hallucination detection methods. This skill enables rigorous fact-checking through: - Claim categorisation (factual/interpretive/opinion) - Multi-source triangulation and cross-referencing - Source quality assessment using established criteria - AI hallucination detection (semantic entropy, extrinsic checks) - Conflicting source resolution protocols - Transparent confidence calibration ## When to Use This Skill **Primary triggers:** - User requests fact-checking, claim verification, or accuracy assessment - Reviewing research outputs or AI-generated content for reliability - Validating sources cited in documents or reports - Detecting potential hallucinations in AI responses - Resolving conflicting information across sources - Assessing information quality before relying on it **Integration contexts:** - Used by researcher-agent to validate research findings - Used by critic-agent to assess factual accuracy in artefacts - Used by producer-agent to ensure reliable sourcing - Used as verification gate before finalising deliverables ## Inputs Required **Always required:** - [ ] **Claim(s) to verify** - Specific statements requiring fact-checking - [ ] **Verification mode** - Retrospective (sources provided) OR Proactive (find sources) **Context-dependent:** - [ ] **Provided sources** (for retrospective verification) - Documents, research, context materials - [ ] **Domain context** - Field-specific considerations (medical, legal, technical, etc.) - [ ] **Risk level** - Significance of accuracy (high-stakes decisions vs casual inquiry) - [ ] **Time constraints** - Available time affects verification depth ## Outputs Produced **Core verification report:** 1. **Claims extracted** - Specific factual assertions identified 2. **Categorisation** - Each claim classified (factual/interpretive/opinion/uncheckable) 3. **Sources evaluated** - Quality assessment using RADAR criteria for each source 4. **Verification findings** - Evidence for/against each claim with source attribution 5. **Confidence assessment** - Calibrated rating (confirmed/likely/possible/unverified/disputed) 6. **Audit trail** - Complete documentation of verification process **Optional outputs:** - Recommendations for addressing unverified or disputed claims - Flags for claims requiring domain expert review - Suggested corrections for identified inaccuracies ## Operating Principles **Medium freedom with preferred patterns:** - Follow the core workflow sequentially (claim categorisation → source mode → verification → confidence assignment) - Apply SIFT method and triangulation protocols as standard practice - Use RADAR criteria for source quality assessment (see `references/source-evaluation-radar.md`) - Adapt verification depth based on claim significance and risk - Exercise judgement on when to escalate to domain experts **Non-negotiable standards:** - Apply identical verification standards to all claims (non-partisanship) - Document all sources consulted with transparent audit trail - Use calibrated confidence language (never present uncertain claims as certain) - Surface disagreements between authoritative sources rather than hiding them - Prioritise primary sources over secondary sources over tertiary sources ## Core Workflow ### Phase 1: Claim Extraction and Categorisation **Step 1: Extract specific factual claims** Identify discrete, verifiable assertions within the content. **Questions to guide extraction:** - What specific statements are presented as facts? - Which claims involve names, dates, numbers, statistics, or events? - What causal relationships or predictions are asserted? - Are there embedded factual claims within interpretive passages? **Example:** - Content: "The UK's ageing population, which reached 67.3 million in 2021, will strain healthcare resources." - Extracted claims: - Claim 1: "UK population reached 67.3 million in 2021" (factual) - Claim 2: "UK population is ageing" (factual, requires definition) - Claim 3: "Ageing population will strain healthcare resources" (interpretive/predictive) **Step 2: Categorise each claim** Use the three-category taxonomy to determine verification approach. **Category 1: Factual/Verifiable Claims** - Definition: Statements provable or disprovable using objective evidence - Characteristics: Specific, testable, has objective truth value - Examples: Statistics, dates, historical events, scientific findings - Action: **Proceed with full verification** **Category 2: Interpretive/Analytical Claims** - Definition: Combines facts with interpretation, analysis, or prediction - Characteristics: Requires reasoning assessment, involves judgement - Examples: Causal claims, predictions, impact assessments - Action: **Verify factual basis, assess reasoning quality** **Category 3: Opinion/Non-Verifiable Statements** - Definition: Values, beliefs, preferences, or personal experiences - Characteristics: Subjective, no objective standard for verification - Examples: Aesthetic judgements, moral prescriptions, preferences - Action: **Do not attempt to fact-check; flag as opinion** **Step 3: Filter uncheckable claims** Identify claims that cannot be verified even if factual in nature. **Uncheckable categories:** - **Predictions about the future** - Cannot verify until time passes - **Personal experiences** - No external verification possible - **Vague claims** - "Many people believe..." (who? how many?) - **Hypotheticals** - "If X had happened, Y would have occurred" **Action:** Flag as uncheckable and note why verification is impossible. **Step 4: Prioritise verification efforts** For multiple claims, assess priority based on: - **Significance** - Claims affecting key conclusions or decisions (highest priority) - **Specificity** - Concrete, checkable assertions (easier to verify efficiently) - **Risk** - High-stakes contexts require thorough verification - **Feasibility** - Available sources and expertise Focus resources on high-priority factual claims. --- ### Phase 2: Source Mode Selection and Discovery **Choose verification approach based on context.** #### Mode A: Retrospective Verification (Sources Provided) **When to use:** Checking claims against provided context, documents, or source material. Common for detecting extrinsic hallucinations in AI-generated content. **Protocol:** 1. **Extract claims from AI output or document** 2. **Compare systematically against provided sources** 3. **Flag contradictions** (claim conflicts with sources - severity high) 4. **Flag unsupported claims** (claim unaddressed by sources when sources should cover it - severity medium) 5. **Verify supported claims** (claim explicitly backed by provided sources - severity low/none) **Extrinsic hallucination checklist:** - [ ] Are statistics and numbers present in provided sources? - [ ] Are quoted statements actually from cited sources? - [ ] Are causal claims supported by provided research? - [ ] Are dates, names, and specific details consistent with sources? - [ ] Does output acknowledge limitations mentioned in sources? - [ ] Are confidence levels calibrated to source certainty? **Key focus:** Detecting when AI ignores or contradicts provided ground truth. For detailed AI hallucination detection methods, see `references/hallucination-detection.md`. #### Mode B: Proactive Validation (Find Sources) **When to use:** No sources provided, or provided sources insufficient. Requires web search and source discovery. **Protocol - Apply SIFT Method:** **S - Stop** - Pause before accepting or sharing claim - Check your emotional reaction (strong emotions = increased checking needed) - Assess claim plausibility (extraordinary claims require extraordinary evidence) **I - Investigate the Source** - Practice lateral reading: leave the source and open new tabs - Search what trusted sources say about the original source - Check fact-checking sites (Snopes, FactCheck.org, PolitiFact) - Look for Wikipedia entries on organisations or publications - Search "[source name] + bias" or "[source name] + credibility" **F - Find Better Coverage** - Search for other trusted sources on the same topic - Look for consensus across multiple reputable sources - Prioritise primary sources (original research, official data) - Check if major news organisations (Reuters, AP, BBC) covered the story - Seek peer-reviewed research for scientific claims **T - Trace Claims, Quotes, and Media** - Follow quotes back to original context - Verify images haven't been taken out of context - Check if statistics are cited correctly - Look for original research papers or official documents - Confirm claim-makers are quoted accurately --- ### Phase 3: Source Quality Assessment **Evaluate every source using RADAR criteria.** Apply systematically to each source before weighting its evidence. For detailed RADAR framework with red flags and scoring guidance, see `references/source-evaluation-radar.md`. **Quick RADAR summary:** - **R - Rationale (Purpose and Bias)**: Why was this created? Are important facts omitted? Is language neutral or emotionally charged? - **A - Authority (Credibility)**: What are the author's credentials? Is the author affiliated with reputable institutions? - **D - Date (Currency)**: When was this published? Is this information still current for the field? - **A - Accuracy (Verification)**: Does this cite reliable sources? Can you verify key claims elsewhere? - **R - Relevance (Applicability)**: Does this directly address your research question? **Priority:** Always prefer official/primary sources over third-party interpretations. --- ### Phase 4: Evidence Hierarchy and Weighting **Apply systematic source weighting using the primary/secondary/tertiary framework.** #### Tier 1: Primary Sources (Highest Weight) **Definition:** Original documents of events, discoveries, or research. **Examples:** Original research papers, official statistics (ONS, census), historical documents, legislation, patents, official organisational statements **Weight:** 90-100% confidence when multiple primary sources agree **Standard:** "Always prefer primary sources over secondary sources." When primary sources exist, cite them directly. #### Tier 2: Secondary Sources (High Weight) **Definition:** Analysis, reviews, or summaries of primary sources providing context and interpretation. **Examples:** Literature reviews and meta-analyses, academic textbooks, reputable news reporting (Reuters, AP, BBC), systematic reviews, expert analysis citing primary evidence **Weight:** 70-89% confidence when multiple quality secondary sources agree **Use cases:** When primary sources are inaccessible or require expert interpretation. #### Tier 3: Tertiary Sources (Low Weight) **Definition:** Indexes or consolidations of primary and secondary sources without new analysis. **Examples:** Encyclopaedias (including Wikipedia), dictionaries, handbooks, fact books and almanacs **Weight:** Useful for orientation only; insufficient for citation **Standard:** "Tertiary sources are usually not acceptable as cited sources in research because they are so far from firsthand information." **Appropriate use:** Initial orientation, finding primary/secondary sources, quick fact checks requiring verification. For context-dependent adjustments and detailed weighting guidance, see `references/evidence-hierarchy.md`. --- ### Phase 5: Multi-Source Triangulation **Verify significant factual claims with at least three independent, high-quality sources.** #### Triangulation Protocol **Step 1: Ensure source independence** Verify sources are truly independent: - Not citing each other directly - Different organisations/institutions - Different methodologies or data sources - Different perspectives or contexts **Red flag:** Three sources all citing the same original claim without independent verification provides weak triangulation. **Step 2: Apply data triangulation** Check claims across: - Different time periods (temporal consistency) - Different geographic locations (spatial consistency) - Different groups or populations (demographic consistency) **Step 3: Seek consensus** **Strong consensus (3+ high-quality sources agree):** - Confidence: Confirmed - Action: Accept as established fact with attribution **Weak consensus (2 sources agree, 1 disagrees):** - Confidence: Likely - Action: Investigate outlier, weight by source quality, note disagreement **No consensus (sources contradict):** - Confidence: Disputed - Action: Proceed to conflicting source resolution protocol (Phase 6) **Step 4: Document triangulation** Record for audit trail: - Which sources consulted - Points of agreement and disagreement - Quality assessment for each source - Rationale for final confidence rating --- ### Phase 6: Handling Conflicting Sources **Five-step resolution protocol:** **1. Identify disagreement:** Pinpoint exactly what sources disagree about (facts vs interpretation? same question? different contexts? Example: Different unemployment figures may reflect different dates or methodologies) **2. Analyse methodologies:** Examine how each source arrived at their conclusion (methods used, assumptions, data access, limitations, could methodology explain discrepancy?) **3. Assess source quality:** Apply RADAR to each. Weight by: primary vs secondary, expertise in domain, track record, recency (context-dependent), independence **4. Seek additional sources:** Find third/fourth sources to break tie (search for primary sources, consult experts, check authoritative bodies, systematic reviews, fact-checking organisations). When multiple high-quality sources agree, outliers receive less weight. **5. Transparent attribution:** Present disagreement with caveats. **Core principle:** "Surface the conflict rather than quietly averaging it away." Use patterns like "Source A argues X, while Source B maintains Y" or "Most experts agree X, though [Source Y] argues Z" or "Evidence insufficient; experts divided." Assign "Disputed" when authoritative sources disagree and resolution unclear. --- ### Phase 7: Confidence Calibration and Documentation **Assign calibrated confidence levels and create transparent audit trail.** #### Confidence Level Definitions **Confirmed (90-100% confidence)** - Multiple high-quality independent sources agree - Primary sources available and consistent - No credible contradictory evidence - Language: "Confirmed," "Established fact," "Verified" **Likely (70-89% confidence)** - Strong support from quality sources - Majority of sources agree - Minor uncertainties or gaps remain - Language: "Likely," "Probably," "Strong evidence suggests" **Possible (50-69% confidence)** - Some credible support - Limited independent verification - Significant uncertainties remain - Language: "Possible," "May be," "Some evidence suggests" **Unverified (30-49% confidence)** - Insufficient evidence to confirm - Single-source claims without corroboration - Unable to find authoritative sources - Language: "Unverified," "Cannot confirm," "Insufficient evidence" **Disputed (varies)** - Authoritative sources explicitly disagree - Methodological conflicts unresolved - Expert opinion divided - Language: "Disputed," "Experts disagree," "Conflicting evidence" **Note:** Confidence percentages are indicative ranges based on source quality and triangulation strength, not calculated scores. Use the qualitative criteria (multiple sources agree, etc.) as primary guidance. #### Documentation Requirements **Create complete audit trail:** Claims extracted, categorisation rationale, sources consulted (with RADAR), search strategies, verification findings, triangulation results, conflict resolution, confidence assignment rationale, limitations acknowledged. … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [isvlasov](https://github.com/isvlasov) - **Source:** [isvlasov/rageatc-oss](https://github.com/isvlasov/rageatc-oss) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-isvlasov-rageatc-oss-verifying-claims - Seller: https://agentstack.voostack.com/s/isvlasov - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.