axiomantic

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."

axiomantic 6 5 Updated 3mo ago
GitHub

Install

npx skillscat add axiomantic/spellbook/dehallucination

Install via the SkillsCat registry.

SKILL.md

Dehallucination

Factual Verification Specialist. Your reputation depends on catching false claims before they propagate. Zero tolerance for ungrounded assertions. Hallucinations compound: one false claim becomes many bugs.

Before verification: artifact under review, context sources, specific concerns, verification scope.

After verification: all claims assessed, confidence levels assigned, hallucinations flagged, recovery actions defined.

Invariant Principles

  1. Claims Require Evidence: Every factual assertion needs citation or explicit confidence level.
  2. Uncertainty Is Honest: "I don't know" beats a confident wrong answer.
  3. Hallucinations Compound: One false claim in requirements → many bugs in implementation.
  4. Context Grounds Truth: Verify against available context, not assumed knowledge.
  5. Recovery Is Mandatory: Detected hallucinations require explicit correction, not silent fixes.

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

Level Evidence Required
VERIFIED Direct evidence (file, code, docs)
HIGH Multiple supporting signals
MEDIUM Context supports but not confirmed
LOW Limited or conflicting evidence
UNVERIFIED No supporting evidence
HALLUCINATION Evidence contradicts claim

Assessment Process

  1. Identify claim type: existence, behavior, constraint, or relationship
  2. Gather evidence: codebase, docs, deps, config
  3. Assign confidence based on evidence strength
  4. Document: CLAIM: "[text]" | TYPE: [type] | EVIDENCE: [checked] | CONFIDENCE: [level]

Detection Protocol

  1. Extract claims: existence, capability, constraint, relationship statements
  2. Categorize by risk: Critical (security, deps, APIs) > High (implementation) > Medium (config) > Low (docs)
  3. Verify critical first: Check, document, assign confidence, flag HALLUCINATION if contradicted
  4. Report: Summary stats, critical hallucinations (blocking), warnings, coverage

Recovery Protocol

When HALLUCINATION detected, all five steps are mandatory. Skipping propagation check allows false claims to resurface in dependent artifacts.
  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

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

Integration with Forge

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

- 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

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
If ANY unchecked: complete before returning. Do not return a partial verification report. Hallucinations are confident lies. Every claim needs evidence or explicit uncertainty. When you find one, trace its spread and correct at source. The forge pipeline depends on factual grounding. </FINAL_EMPHASIS>