Opus review

Skill tchr-dev/autonovel/.claude/skills/revision/opus-review

Autonomous fantasy-novel pipeline as Claude Code skills, agents, and slash commands

Install

npx -y skills add tchr-dev/autonovel --skill opus-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Send the full manuscript to two parallel personas — literary critic + professor of fiction — for deep prose-level review. Parses the result with scripts/parse_review.py to extract numbered actionable items with severity and qualification status. Decides whether to keep revising or stop based on stopping conditions. Use after automated revision cycles plateau.

SKILL.md

3.1 KB, as published. Nobody here has run it

Opus review loop

This is the deepest evaluation. It catches what automated tools miss: prose-level repetition, character thinness, ethical gaps, structural monotony.

Procedure

Step 1 — build the manuscript

python scripts/build_manuscript.py <novel-dir>

Writes <novel-dir>/manuscript.md (concatenated chapters separated by ---).

Step 2 — spawn two subagents in parallel

In a single Agent tool call (parallel):

subagent_type: literary-critic — full manuscript inline. Returns plain-prose newspaper-style review.
subagent_type: professor-of-fiction — full manuscript inline. Returns numbered actionable items.

For very long manuscripts (>200k chars), pass manuscript.md to each agent and let it Read the file from its own context.

Step 3 — assemble the review document

Write to <novel-dir>/reviews/<timestamp>_review.md:

# Review — <novel title> — <timestamp>

## Literary critic

<critic agent output>

## Professor of fiction

<professor agent output>

Step 4 — parse

python scripts/parse_review.py <novel-dir>/reviews/<timestamp>_review.md > <novel-dir>/reviews/<timestamp>_parsed.json

This extracts:

star rating (if present)
numbered professor items, each with severity (major/moderate/minor), type (compression/addition/structural/mechanical/revision), qualified (boolean), suggestion
aggregate counts
stop (boolean) and stop_reason

Step 5 — stopping conditions

Stop revising if any of:

★★★★½ rating with 0 major items
≥4 stars with >50% of items qualified/hedged
≤2 items found total

The parser sets stop: true automatically when these fire.

Step 6 — if not stopping

Print the top 3 unqualified major/moderate items. For each one the user / orchestrator wants to address:

gen-brief --auto (or with the specific item) → produces a revision brief
gen-revision <ch> with the brief → rewrites the chapter
Mechanical fixes via apply-cuts for pattern-level issues
evaluate-chapter <ch> to confirm improvement
Commit-style snapshot of the novel directory

Then re-run opus-review for the next round.

Severity heuristics from the Bells production

Multiple major items → structural work needed
Few major, some moderate → targeted revisions, 2-3 more rounds
All moderate/minor → polish only, 1-2 more rounds
Mostly qualified hedges → done; ship it

Items that persist across 3+ rounds may be structural to the novel's voice/approach, not bugs. Learn to accept them. The reviewer will ALWAYS find something.

Output to user

Print: stars (if any), total / major / qualified counts, stop verdict and reason. If continuing, list the top 3 actionable items with chapter references.