Agent-Skills.md

Agent Skills: Dehallucination

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).

UncategorizedID: axiomantic/spellbook/dehallucination

Repository

axiomantic/spellbook
axiomanticLicense: MIT
105

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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 the installed spellbook rule modules.</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>