voice-review

Testing & Quality

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

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How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/athola/claude-night-market/blob/HEAD/plugins/scribe/skills/voice-review/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/voice-review/. Do not write files or run scripts until I approve.

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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