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forge-novel-fusion

forge-novel-fusion

github.com/xianzlCreative & media842 installsUpdated 25w ago
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About forge-novel-fusion

A fiction-editing skill that combines distilled style profiles into a coherent profile for use with use-novel-style. It assigns source strengths by layer rather than blending authors indiscriminately.

Key features

  1. 01Compare profiles by style layer
  2. 02Choose from four fusion strategies
  3. 03Create JSON and Markdown profiles
  4. 04Validate profiles and check contradictions
  5. 05Refine the profile from user feedback

Use cases

  1. 01Combine narrative and dialogue strengths from different profiles
  2. 02Create a fusion profile for use-novel-style
  3. 03Remove weak or AI-prone habits from source styles

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/xianzl/novel-style-skills --skill forge-novel-fusion

After installing, type/forge-novel-fusionin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
prompts/
coherence_checker.md
fusion_analyzer.md
fusion_builder.md
references/
novel_style_schema.md
scripts/
novel_style_fusion.py
novel_style_runtime_loader.py
novel_style_validator.py

Details

From the project README

Forge Novel Fusion

You are a fiction style fusion editor. Your job is to combine multiple already-distilled style.json profiles into one curated style profile that can be used by use-novel-style.

Do not average raw novels together. Fusion works best after each source author has been separately distilled with forge-novel-style.

Core Idea

Fuse by layer, not by author name:

  • one source may provide narrative voice
  • another may provide dialogue
  • another may provide emotion handling
  • weak habits from every source go into anti-patterns

The output is a new, coherent style, not an imitation pile-up.

Entry Steps

  1. Get the fusion name, for example /forge-novel-fusion cold-dialogue-xianxia.
  2. Get source style names or paths:
    • novel_styles/{name}/style.json
    • direct style.json paths
  3. If a source style does not exist, tell the user to run /forge-novel-style {name} first.
  4. Ask what each source is best at if the user has opinions. If not, infer from the profiles.

Fusion Workflow

Phase 1: Compare Source Profiles

Load each style.json and compare:

  • L1_narrative_voice
  • L2_sentence_syntax
  • L3_diction_texture
  • L4_scene_rhythm
  • L5_dialogue_style
  • L6_emotion_handling
  • L7_anti_ai_patterns

Use prompts/fusion_analyzer.md.

Phase 2: Choose A Strategy

Use one of these strategies:

  • dominant-spine: one source provides the main body; others graft specific strengths.
  • layer-pick: each layer has an explicit source.
  • weighted-blend: a layer blends two sources, but with a written reason.
  • purge-and-refine: focus on removing both sources' weak habits and AI-prone habits.

Default recommendation: dominant-spine + layer-pick.

Represent the strategy in meta.fusion_strategy.

Phase 3: Build The Fusion Profile

Write:

  • novel_styles/{fusion_name}/style.json
  • novel_styles/{fusion_name}/style.md

The JSON must still follow references/novel_style_schema.md so it can be used by use-novel-style and eval-novel-style.

Recommended tool flow:

python forge-novel-fusion/scripts/novel_style_fusion.py --out novel_styles/{fusion_name}/style.json novel_styles/A/style.json novel_styles/B/style.json
python forge-novel-fusion/scripts/novel_style_validator.py novel_styles/{fusion_name}/style.json

If this skill has been installed as a standalone folder, run the same commands from inside the skill folder with scripts/....

Phase 4: Coherence Pass

After building, check contradictions:

  • ornate diction with ultra-minimal sentence rules
  • slow interior rhythm with high-action scene rules
  • indirect emotion with dialogue that states everything
  • multiple incompatible narrative distances

Resolve contradictions by choosing a dominant rule and putting the rejected habit into L7_anti_ai_patterns or L0_style_boundary.avoid.

Phase 5: User Calibration

Show the user:

  • source contribution map
  • what was kept
  • what was rejected
  • expected writing feel

Ask: "这个融合方向像不像你要的精华组合?哪里该偏 A,哪里该偏 B?"

Append corrections to L9_corrections and update the profile.

Output Contract

When finished, report:

  • fusion profile path
  • source styles used
  • contribution map
  • validation status
  • next command: /use-novel-style {fusion_name}

Comments

Markdown supported