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Style Harmonizer

Community
WILLOSCAR
style-harmonizer

De-slot and harmonize paper voice across `sections/*.md` without changing meaning or citation keys.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namestyle-harmonizer
Stars
513
Forks
39
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Style Harmonizer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/style-harmonizer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/style-harmonizer .claude/skills/style-harmonizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Style Harmonizer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Style Harmonizer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Style Harmonizer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Style Harmonizer (de-slot editor)

Triggers & routing

  • Trigger: style harmonizer, de-template stems, remove slot phrases, discourse stems, 写作风格统一, 去槽位句式, 去生成器味.
  • Use when: writer-selfloop is PASS but output/WRITER_SELFLOOP_TODO.md flags Style Smells (e.g., repeated count-based openers), or the draft reads like many sections share the same rhythm.

Purpose: remove subtle generator-voice signals that can survive structural gates.

This skill is not a full rewrite. It is a targeted rewrite queue:

  • only touch the specific sections/*.md files flagged under ## Style Smells
  • keep facts and citation keys unchanged

Inputs

Required:

  • output/WRITER_SELFLOOP_TODO.md (Style Smells section)
  • the referenced sections/*.md files

Optional (helps you stay in-scope while rewriting):

  • outline/writer_context_packs.jsonl (allowed citations + opener_mode hints)

Output

Run this targeted pass after writer-selfloop and before opener-variator and logic polish. If major edits happened since the report, rerun writer-selfloop so ## Style Smells reflects current text. The deterministic script is a certification adapter: it refuses to create the marker while the latest report still names flagged files or predates any sections/*.md file. Perform the semantic rewrite through this Skill or the responsible upstream writer, rerun writer-selfloop, then retry the adapter. A passing marker records the certified Section-tree SHA256.

  • Updated sections/*.md files (same filenames; still body-only; no headings)
  • Re-running writer-selfloop is the audit trail (Style Smells should shrink).
  • Create sections/style_harmonized.refined.ok (empty file) when you are done (pipeline contract signal; required if this unit is marked DONE).

Role cards (use explicitly)

Style Harmonizer (editor)

Mission: remove slot phrases and stem repetition while keeping meaning unchanged.

Do:

  • rewrite the surface form (opener/closer/cadence), not the claim
  • keep each paragraph content-bearing (argument bridge, not navigation)
  • prefer small local edits over global style refactors

Avoid:

  • adding new factual claims or new citations
  • moving citations to different paragraphs or different subsections
  • rewriting a thin section instead of routing upstream for more evidence

Evidence Steward (skeptic)

Mission: prevent style work from becoming content drift.

Do:

  • after each rewrite, spot-check that every cited claim still matches the same sentence
  • if you feel forced to add new material to make prose sound better, stop and route upstream

Common style smells and how to fix them

1) Count-based opener slots (Two limitations..., Three takeaways...)

Why it is high-signal: it creates a reusable sentence slot that repeats across H3s.

Rewrite moves (choose one):

  • Integrate the caveat into a contrast paragraph (last sentence): state the boundary that changes interpretation.
  • Use a single caveat sentence opener (no counting), but rotate across H3s (avoid repeating the same stem): "These results hinge on ..." / "Interpretation depends on ..." / "Evidence is thin when ..." / "A caveat is that ..." (use sparingly).
  • If enumeration is truly needed, hide the count: use two coordinated clauses in one sentence, or vary the syntax (do not repeat across sections).

Mini example (paraphrase only):

  • Bad: Two limitations stand out. First, ...
  • Better: These results hinge on ...; this matters because it changes how results transfer across protocols.

2) Reused discourse stems (The key point is that ...)

Rewrite moves:

  • Replace with one of: "A practical implication is that ...", "One takeaway is that ...", "A useful way to read these results is ...".
  • Change cadence: split into a short claim sentence plus a follow-up sentence with the condition/why.

3) Same opener cadence across many H3s

Rewrite moves:

  • Switch opener mode for the section (tension-first / decision-first / protocol-first / contrast-first).
  • Replace generic connectors (Additionally/Moreover) with content-bearing pivots ("At the protocol level, ...", "Under budget constraints, ...").

4) Overview / narration openers ("This section provides an overview ...")

Why it is high-signal: it reads like a generated ToC narration rather than a paper argument.

Rewrite moves:

  • Replace "overview" narration with a content-bearing lens: tension/decision/failure/protocol/contrast.
  • Keep the first sentence falsifiable: name the constraint and why it matters (not what you are about to do).

Mini example (paraphrase only):

  • Bad: This section provides an overview of tool interfaces for agents.
  • Better: Tool interfaces define what actions are executable; interface contracts therefore determine which evaluation claims transfer across environments.

5) Paragraphs repeatedly starting with connector adverbs (Moreover, In addition, Therefore, Overall, As a result)

Why it is high-signal: the prose starts to sound mechanically stitched (each paragraph begins with the same connective), even when the content is solid.

Rewrite moves:

  • Keep the logical relation, but move the connector into the sentence: start with the subject (e.g., "Tool catalogs also ..."), then add the relation mid-sentence ("..., which in turn ...").
  • Use clause shapes instead of adverb openers: "While ... , ..." / "Although ... , ..." / "Because ... , ...".
  • When summarizing, avoid "Overall," as a default label; state the conclusion directly as a claim sentence.

Mini example (paraphrase only):

  • High-signal smell: "Moreover, ..." (repeated across multiple paragraphs)
  • Better: start with the content noun phrase ("One implication is ..." / "A practical constraint is ..."), then express the relation inside the sentence.

6) Internal shorthand leaking into paper voice ("token(s)" used as a protocol noun)

Why it is high-signal: outside of NLP contexts (token budget/context window), "token" reads like internal shorthand. In this pipeline it often originates from packs/schemas and gets copied into prose, which makes the draft feel like an intermediate artifact.

Rewrite moves:

  • Replace "X tokens" with reader-facing nouns: "X protocol details/assumptions/fields/parameters/dimensions".
  • If you truly mean language-model tokens, keep it numeric and specific (e.g., "a 60k-token context window", "token budget"); avoid using "token" as a generic label for protocol metadata.
  • Avoid "three tokens: ..." slots; either (a) state the conclusion directly, or (b) use "three reporting fields" and embed them naturally in the sentence.

Mini example (paraphrase only):

  • Bad: Overall, self-improvement should be reported as a protocol with three explicit tokens: the feedback channel, the update rule, and the accounting rule.
  • Better: Self-improvement results are easiest to compare when papers make three reporting fields explicit: the feedback channel, the update rule, and the accounting assumptions.

Workflow (minimal)

  1. Read output/WRITER_SELFLOOP_TODO.md
  • Find ## Style Smells and the file list.
  1. Rewrite only the flagged files
  • Prefer fixing the upstream writer skill that created the cadence problem rather than relying on a local regex cleanup.
  • If a local rewrite is still necessary, make small edits: opener/closer stems, sentence shape, connector variety.
  • Best-of-2 rewrite (recommended): for any paragraph you touch, draft 2 alternative phrasings and keep the one that (a) removes the slot stem, and (b) does not introduce a new repeated cadence across H3s.
  • If needed, consult outline/writer_context_packs.jsonl for opener_mode hints and to stay citation-scope safe while rewriting.
  • Do not touch citation keys.
  1. Re-run writer-selfloop
  • Expect: PASS remains PASS.
  • Expect: Style Smells section is shorter (or disappears).

Done checklist

  • The same slot phrase does not repeat across multiple H3s (especially count-based openers).
  • No citation keys were added/removed/moved.
  • writer-selfloop still reports PASS, and Style Smells shrinks.

Script

Quick Start

  • uv run python .codex/skills/style-harmonizer/scripts/run.py --workspace <workspace>

All Options

  • --workspace <dir> (required)
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Harmonize style in a survey workspace:
    • uv run python .codex/skills/style-harmonizer/scripts/run.py --workspace workspaces/survey-llm-agents

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Style Harmonizer AI skill do?

De-slot and harmonize paper voice across `sections/*.md` without changing meaning or citation keys.

Why use Style Harmonizer on TypingMind?

Because you install it once and use it with any model. Style Harmonizer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Style Harmonizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/style-harmonizer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Style Harmonizer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Style Harmonizer?

As many as you like. As long as a model supports skills, you can use Style Harmonizer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Style Harmonizer AI skill free?

It is published on GitHub by WILLOSCAR. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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