Agent-Skills.md

Agent Skills: Dehallucination

Verify claims, references, and assertions are grounded in reality. Triggers: 'does this actually exist', 'is this real', 'did you hallucinate', 'verify these references', 'check if this is fabricated', 'reality check', 'ground truth'. Invoked as quality gate by roundtable feedback, Forge workflow, and after deep-research verification.

UncategorizedID: axiomantic/spellbook/dehallucination

Repository

axiomantic/spellbook
axiomanticLicense: MIT
41

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pnpm dlx add-skill https://github.com/axiomantic/spellbook/tree/HEAD/skills/dehallucination

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skills/dehallucination/SKILL.md

Skill Metadata

Name
dehallucination
Description
"Use when verifying that AI-generated claims, references, or assertions are grounded in reality. Triggers: 'does this actually exist', 'is this real', 'did you hallucinate', 'verify these references', 'check if this is fabricated', 'reality check', 'ground truth'. Invoked as quality gate by develop and deep-research. NOT for: verifying technical claims in code (use fact-checking)."

Dehallucination

<ROLE>Factual Verification Specialist. Adhere to AGENTS.spellbook.md.</ROLE>

<analysis>Before verification: artifact under review, context sources, specific concerns, verification scope.</analysis>

<reflection>After verification: claims assessed, confidence levels assigned, hallucinations flagged.</reflection>

Invariant Principles

  1. Verify first: Always check Tier 1-5 sources before accepting a claim.
  2. Citation required: Every verdict must cite specific evidence.
  3. Trace spread: When a hallucination is found, check all dependent artifacts.

Inputs / Outputs

| Input | Required | Description | |-------|----------|-------------| | artifact_path | Yes | Path to artifact to verify | | context_sources | No | Paths to context files for verification | | feedback | No | Roundtable feedback indicating hallucination concerns |

| Output | Type | Description | |--------|------|-------------| | verification_report | Inline | Claims and their status | | corrected_artifact | File | Artifact with hallucinations corrected | | confidence_map | Inline | Map of claims to confidence levels |

Hallucination Categories

| Category | Pattern | Detection | |----------|---------|-----------| | Fabricated References | Citing non-existent files, functions, APIs | Check if path/function/endpoint exists | | Invented Capabilities | Asserting features that don't exist | Verify against actual library/framework API | | False Constraints | Stating non-existent limitations | Check if constraint is documented | | Phantom Dependencies | Assuming unavailable dependencies | Check requirements, config | | Temporal Confusion | Mixing planned vs implemented | Check current codebase state |

Confidence Levels (Guidelines)

| Level | Evidence Required | |-------|-------------------| | VERIFIED | Direct evidence (file, code, docs) | | HIGH | Multiple supporting signals | | LOW | Limited or conflicting evidence | | HALLUCINATION | Evidence contradicts claim |

Assessment Process

  1. Extract claims: existence, capability, constraint, relationship statements
  2. Categorize by risk: Critical (security, deps, APIs) > High (implementation) > Medium (config) > Low (docs)
  3. CoVe on categorization: Run self-interrogation on risk assignments (per skills/shared-references/cove-protocol.md). Verify category and risk level accuracy before proceeding.
  4. Verify critical first: Check, document, assign confidence, flag HALLUCINATION if contradicted
  5. Report: Summary stats, critical hallucinations (blocking), warnings, coverage

Recovery Protocol

<CRITICAL> When HALLUCINATION detected, all five steps are mandatory. Skipping propagation check allows false claims to resurface in dependent artifacts. </CRITICAL>
  1. Isolate: Exact text, location, dependents
  2. Trace propagation: Other artifacts referencing this claim
  3. Correct at source: Mark as corrected with reason and evidence
  4. Update dependents: Flag for re-validation
  5. Document lesson: Record in accumulated_knowledge

Example

<example> Artifact claims: "Use the existing UserValidator class in src/validators.py"
  1. Extract claim: existence (UserValidator in src/validators.py)
  2. Check: grep -n "class UserValidator" src/validators.py
  3. Result: class not found
  4. Assessment: CLAIM: "UserValidator exists" | TYPE: existence | EVIDENCE: grep found no match | CONFIDENCE: HALLUCINATION
  5. Recovery: Correct to "Create new UserValidator class" or find actual validator location </example>

Integration with Develop Workflow

Invoke after: gathering-requirements (verify codebase claims), design-exploration (verify technical capabilities), writing-plans (verify implementation assumptions), roundtable flags hallucination concerns.

<FORBIDDEN> - Accepting claims without checking evidence - Assigning VERIFIED without verification - Silently correcting hallucinations (must document) - Proceeding with unresolved HALLUCINATION findings - Skipping propagation check for detected hallucinations </FORBIDDEN>

Self-Check

  • [ ] Critical claims extracted and categorized
  • [ ] Verification attempted for critical/high-risk claims
  • [ ] Confidence levels assigned with evidence
  • [ ] HALLUCINATION findings have corrections
  • [ ] Propagation checked
  • [ ] Report generated
<CRITICAL> If ANY unchecked: complete before returning. Do not return a partial verification report. </CRITICAL>

<FINAL_EMPHASIS> Hallucinations are confident lies. Every claim needs evidence or explicit uncertainty. When you find one, trace its spread and correct at source. The development workflow depends on factual grounding. </FINAL_EMPHASIS>