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Voice Review Skill for Claude

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

1k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
324
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/athola/claude-night-market --skill voice-review

The instruction itself

18 sections, as written by the author

Voice Review Skill

Dispatch dual review agents and present unified findings.

When NOT To Use

  • Producing the text (use scribe:voice-generate)
  • A generic AI-pattern scan with no voice profile (use

scribe:slop-detector)

Method: Parallel Dual-Gate Review

Two agents run in parallel on the generated text:

  • Prose reviewer: AI patterns, banned phrases, voice drift
  • 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

  • voice-review:text-loaded - Generated text read
  • voice-review:register-loaded - Voice register loaded
  • voice-review:agents-dispatched - Both reviewers launched
  • voice-review:hard-fails-fixed - Auto-corrections applied
  • voice-review:advisories-presented - Tables shown to user
  • voice-review:findings-verified - Citations confirmed by verifier

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 | Anchor | 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:

  • voice-generate produces text
  • voice-generate calls voice-review
  • voice-review dispatches agents, processes results
  • User makes decisions on advisories
  • 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

Verify Findings Are Grounded (voice-review:findings-verified)

Every advisory row must cite a real line and a verbatim anchor. Write

findings to .review/findings.json and confirm each citation resolves:

python plugins/imbue/scripts/citation_verifier.py \
  --findings .review/findings.json --repo-root .

Drop or label UNVERIFIED any finding the verifier fails (exit 1); only

verified findings enter the advisory tables. See Skill(imbue:review-core)

Step 5 and Skill(imbue:structured-output) for the schema.

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)

Exit Criteria

  • Both review agents returned results without timeout
  • 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
  • Every advisory row carries a Line (file:line) and verbatim Anchor;

citation_verifier.py confirmed all citations (exit 0) or unverified

rows are dropped/labeled UNVERIFIED

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

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How to use it

Copy the folder

Take athola/voice-review from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.