Voice Validator Skill
Operator Context
This skill operates as an operator for voice validation workflows, configuring Claude's behavior for rigorous critique-and-rewrite enforcement. It implements the Iterative Refinement architectural pattern -- scan content, identify violations, revise, rescan -- with Domain Intelligence embedded in voice-specific negative prompt checklists and pass/fail criteria.
Hardcoded Behaviors (Always Apply)
- CLAUDE.md Compliance: Read and follow repository CLAUDE.md before validating
- Over-Engineering Prevention: Fix only voice violations. No content rewriting beyond voice fidelity
- Scan Before Revise: NEVER revise content without first scanning against the full checklist
- Evidence Required: Every violation must cite a specific quote from the content
- Maximum 3 Iterations: If still failing after 3 rewrites, output with flagged concerns
- Preserve Intent: Revisions fix voice violations only -- never alter meaning or substance
Default Behaviors (ON unless disabled)
- Full Checklist Scan: Run all categories (tone, structure, sentence, language, emotion, questions, metaphors)
- Violation Report: Output structured validation report with quoted violations and fixes
- Auto-Revise on Fail: Automatically produce revised version when violations detected
- Rescan After Revision: Re-run checklist against revised content to confirm pass
- LinkedIn Test: Apply quick "could this be posted on LinkedIn without edits?" heuristic
- Mode Detection: Identify voice mode (1-5) for mode-specific checks
Optional Behaviors (OFF unless enabled)
- Script Validation: Run for deterministic banned-pattern detection
- Inline Validation: Silent self-check within conversation without full report
- Cross-Voice Comparison: Compare output against wrong-voice patterns to verify distinctness
What This Skill CAN Do
- Validate content against voice-specific negative prompt checklists
- Identify specific violations with quoted evidence and category labels
- Revise content to fix voice violations while preserving intent
- Enforce iterative scan-revise-rescan loops up to 3 iterations
- Distinguish between different voice profiles with mode-specific criteria
What This Skill CANNOT Do
- Generate content in a target voice from scratch (use the appropriate voice skill instead)
- Create or modify voice profiles (use voice_analyzer.py instead)
- Edit content for non-voice concerns like grammar or accuracy (use anti-ai-editor instead)
- Skip the scan phase and go straight to rewriting
- Validate voices that have no defined checklist
Instructions
Phase 1: IDENTIFY TARGET
Goal: Determine the voice, mode, and content to validate.
Step 1: Identify voice target
- Determine target voice from context or user instruction
- Identify mode if applicable -- casual modes may have additional specific checks
Step 2: Load content
- Read the content to validate
- Note content length -- longer content is more prone to drift
Gate: Voice target and mode identified. Content loaded. Proceed only when gate passes.
Phase 2: SCAN
Goal: Run full checklist against content and identify all violations.
Step 1: Run negative prompt checklist
Check all categories against the target voice's checklist:
- Tone: Does the tone match the voice profile? (e.g., too polished, too corporate, missing warmth)
- Structure: Does the structure match? (e.g., front-loaded constraints, clean outlines, wrap-ups)
- Sentences: Do sentence patterns match? (e.g., dramatic short sentences, rhetorical flourishes, symmetrical structure)
- Language: Any banned words? (amazing, terrible, revolutionary, perfect, game-changing, transformative, incredible, outstanding, exceptional, groundbreaking), marketing/hype, inspirational, unnecessary superlatives
- Emotion: Does emotion handling match? (e.g., explicitly named emotions, venting/ranting, moralizing)
- Questions: Do question patterns match? (e.g., open-ended brainstorming, vague curiosity)
- Metaphors: Do metaphor patterns match? (e.g., journey/path, biological/growth, narrative/story)
Step 2: Check pass conditions
Verify the content matches the target voice's positive identity markers. Common pass conditions include:
- Feels like the person actually wrote it
- Voice-specific patterns are present (thinking out loud, warmth, precision, etc.)
- Could NOT be posted on LinkedIn without edits (for casual voices)
- Does NOT sound like AI wrote it
- Mode-specific patterns are present (casual modes: no preamble, no wrap-up; formal modes: structured flow)
Step 3: Document violations
For each violation, record:
- Category (tone, structure, sentence, language, emotion, question, metaphor)
- Quoted text from the content
- Specific fix recommendation
Gate: Full checklist scanned. All violations documented with evidence. Proceed only when gate passes.
Phase 3: REVISE
Goal: Fix all violations while preserving content intent and substance.
Step 1: Apply fixes
- Address each violation with the smallest change that resolves it
- Preserve the original meaning and information
- Maintain natural flow -- fixes should not create new violations
Step 2: Verify no overcorrection
- Ensure revisions did not strip necessary content
- Confirm the substance and technical accuracy remain intact
Gate: All documented violations addressed. Intent preserved. Proceed only when gate passes.
Phase 4: VERIFY
Goal: Confirm revised content passes all checks.
Step 1: Rescan revised content
Run the full checklist from Phase 2 against the revised version.
Step 2: Evaluate result
- If PASS: Output final content with validation report
- If FAIL and iteration < 3: Return to Phase 3 with new violations
- If FAIL and iteration = 3: Output content with flagged remaining concerns
Step 3: Output validation report
VOICE VALIDATION: [Voice Name] Mode [mode]
SCAN RESULT: [PASS/FAIL]
VIOLATIONS DETECTED: [N]
ITERATION: [1-3]
[If violations:]
1. [Category]: "[quoted violation]"
Fix: [specific correction]
2. [Category]: "[quoted violation]"
Fix: [specific correction]
REVISED OUTPUT:
[Corrected content]
RESCAN RESULT: [PASS/FAIL]
Gate: Content passes all checks, or maximum iterations reached with flagged concerns. Validation complete.
Examples
Example 1: Technical Voice Validation
User says: "Validate this draft is in the right voice"
Actions:
- Identify target voice from context, determine mode from content style (IDENTIFY TARGET)
- Run full 7-category negative prompt checklist, find 2 violations (SCAN)
- Fix "I'm excited to share" (named emotion) and "This changes everything" (dramatic short sentence) (REVISE)
- Rescan revised content, confirm PASS (VERIFY)
Result: Clean content with validation report
Example 2: Community Voice Validation
User says: "Does this sound like the right voice?"
Actions:
- Identify target voice from context (IDENTIFY TARGET)
- Scan against voice checklist, find missing warmth and no sensory details (SCAN)
- Add experiential language and warmth while preserving substance (REVISE)
- Rescan, confirm warmth and sensory details present, PASS (VERIFY)
Result: Content matches voice profile
Error Handling
Error: "Voice Target Unclear"
Cause: Content doesn't specify which voice to validate against, or context is ambiguous
Solution:
- Check conversation context for voice mentions
- Look for voice-specific patterns to infer target
- If still unclear, ask user to specify voice name and mode
Error: "Violations Persist After 3 Iterations"
Cause: Fundamental mismatch between content substance and voice requirements, or conflicting checklist items
Solution:
- Output content with clearly flagged remaining violations
- List specific checklist items that resist correction
- Suggest the content may need to be regenerated from scratch with the correct voice skill
Error: "Revision Introduced New Violations"
Cause: Fixing one category created violations in another (e.g., removing dramatic sentences introduced polished phrasing)
Solution:
- Address new violations in next iteration
- If oscillating between two violation types, fix both simultaneously
- Prioritize tone and language violations over structural ones
Anti-Patterns
Anti-Pattern 1: Revising Without Scanning
What it looks like: "This doesn't sound right, let me rewrite it" without running the checklist
Why wrong: Subjective assessment misses specific violations. May "fix" things that aren't broken while missing real issues.
Do instead: Complete Phase 2 scan with documented violations before any revision.
Anti-Pattern 2: Over-Revising Beyond Voice
What it looks like: Rewriting entire paragraphs, changing arguments, adding new points during voice correction
Why wrong: Voice validation fixes voice only. Changing substance is scope creep that alters the author's intent.
Do instead: Make the smallest change that resolves each voice violation. Preserve all meaning.
Anti-Pattern 3: Skipping the Rescan
What it looks like: "I fixed the violations, it should be fine now" without re-running the checklist
Why wrong: Fixes can introduce new violations. "Should be fine" is a rationalization.
Do instead: Always run Phase 4 rescan. Every revision gets a full checklist pass.
Anti-Pattern 4: Passing Content That Sounds Like LinkedIn
What it looks like: Content is polished, quotable, and shareable -- but marked as PASS
Why wrong: The LinkedIn test catches ~80% of voice violations. If it reads well on LinkedIn, it fails the target voice.
Do instead: Apply the quick check: "Could this be posted on LinkedIn without edits?" If yes, it FAILS.
References
This skill uses these shared patterns:
- Anti-Rationalization - Prevents shortcut rationalizations
- Verification Checklist - Pre-completion checks
Domain-Specific Anti-Rationalization
| Rationalization | Why It's Wrong | Required Action |
|---|
| "It sounds close enough" | Close enough ≠ voice fidelity | Run full checklist, fix all violations |
| "Only one small violation" | One violation breaks immersion | Fix it. No exceptions |
| "The substance matters more than voice" | Voice IS the deliverable in this context | Complete all 4 phases |
| "I already know what's wrong" | Knowing ≠ documenting with evidence | Scan and cite specific quotes |
Related Skills
- - Generates content in a specific voice (validate output with this skill)
- - Complementary anti-AI pattern detection
- - Multi-step voice generation pipeline that invokes this skill