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AI/MLathola

voice-review

Runs parallel prose and craft review agents against a voice profile. Use when checking generated content for AI patterns and voice drift before publishing.

Stars
294
Source
athola/claude-night-market
Updated
2026-05-30
Slug
athola--claude-night-market--voice-review
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/athola/claude-night-market/HEAD/plugins/scribe/skills/voice-review/SKILL.md -o .claude/skills/voice-review.md

Drops the SKILL.md into .claude/skills/voice-review.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Voice Review Skill

Dispatch dual review agents and present unified findings.

Method: Parallel Dual-Gate Review

Two agents run in parallel on the generated text:

  1. Prose reviewer: AI patterns, banned phrases, voice drift
  2. Craft reviewer: Naming, destinations, dwelling, devices, anchoring

Hard failures (banned phrases, em dashes) are auto-fixed. Everything else returns as advisory tables for user decision.

Required TodoWrite Items

  1. voice-review:text-loaded - Generated text read
  2. voice-review:register-loaded - Voice register loaded
  3. voice-review:agents-dispatched - Both reviewers launched
  4. voice-review:hard-fails-fixed - Auto-corrections applied
  5. voice-review:advisories-presented - Tables shown to user

Step 1: Load Context

Read:

  • The generated text (from file or clipboard)
  • The active voice register
  • The banned phrases list

Step 2: Dispatch Review Agents

Launch both agents in parallel:

Agent(prose-reviewer):
  - text: {generated_text}
  - register: {register_content}
  - banned_phrases: {banned_list}

Agent(craft-reviewer):
  - text: {generated_text}
  - register: {register_content}

Step 3: Process Results

Hard Failures

Apply all auto-fixes from prose reviewer silently:

  • Remove/replace banned phrases
  • Replace em dashes with appropriate punctuation
  • Rewrite negation-correction patterns

Report: "Fixed N hard failures (X banned phrases, Y em dashes, Z patterns)"

Advisory Tables

Present both tables to the user:

Prose Review Advisories:

# Line Pattern Current Proposed fix

Craft Review:

Dimension Rating Notes Proposed improvement

Step 4: User Decision

For each advisory row, user can:

  • Accept (a): Apply the proposed fix
  • Reject (r): Keep the current text
  • Rewrite (w): Apply a custom fix

Present as:

[1] Prose: Frictionless transition at "Furthermore, the..."
    Proposed: Cut transition, start mid-thought
    [a]ccept / [r]eject / re[w]rite?

Step 5: Apply Decisions

  • Apply accepted fixes to the text
  • Skip rejected items
  • For rewrites, incorporate user's version
  • Save final text

Step 6: Snapshot (if learning active)

If the user has learning mode enabled:

  • Save "post-review" snapshot (text after hard-fail fixes, before user decisions on advisories)
  • Save "post-fixes" snapshot (text after user decisions)
  • Both go to ~/.claude/voice-profiles/{name}/learning/snapshots/

Integration with voice-generate

When dispatched from voice-generate, the flow is:

  1. voice-generate produces text
  2. voice-generate calls voice-review
  3. voice-review dispatches agents, processes results
  4. User makes decisions on advisories
  5. If learning mode: snapshots saved for later comparison

Standalone Usage

Can also be run on any existing text:

/voice-review path/to/file.md --profile myvoice --register casual

Verification

After the review completes, validate these conditions:

  • Both review agents returned results (no timeouts)
  • Hard failures auto-fixed and diff shown to user
  • Advisory tables presented with accept/reject/rewrite options
  • User decisions applied to the final text
  • Final text saved to disk
  • Snapshots saved (if learning mode active)

Test Spec

The test suite (test_voice_review.py) validates:

  • Skill file exists and references parallel dispatch
  • Hard failure vs advisory separation is documented
  • Prose reviewer agent exists with hard-failure patterns
  • Craft reviewer agent exists with five-dimension ratings
  • Both agents produce tabular output for downstream merging