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Section Logic Polisher

Community
WILLOSCAR
section-logic-polisher

Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namesection-logic-polisher
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 Section Logic Polisher 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/section-logic-polisher
  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/section-logic-polisher .claude/skills/section-logic-polisher
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Section Logic Polisher 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 Section Logic Polisher 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 Section Logic Polisher 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.

Section Logic Polisher (thesis + argument bridges)

Triggers & routing

  • Trigger: logic polisher, section logic, thesis statement, connectors, 段落逻辑, 连接词, 论证主线, 润色逻辑.
  • Use when: sections/S*.md exist but read like paragraph islands; you want a targeted, debuggable self-loop before section-merger.

Purpose: close the main “paper feel” gap that remains even when a subsection is long and citation-dense:

  • missing/weak thesis (paragraph 1 never commits to a claim)
  • weak inter-paragraph flow (paragraph islands; no content-bearing bridges)

This is a local, per-H3 polish step that happens after drafting and before merging.

Note: if the main problem is paragraph-count overgrowth, run paragraph-curator after this check. It only merges adjacent paragraph boundaries and preserves all prose; semantic redundancy still belongs to the owning writer.

What this skill blocks on (and what it does not)

Blocking (must fix):

  • paragraph 1 lacks an explicit thesis / takeaway (a content claim)

Non-blocking (diagnostic only):

  • connector word counts (e.g., “moreover/however/therefore”). Counts are a proxy for paragraph islands, but forcing them as a quota often creates “generator cadence” (paragraph-initial adverbs). Treat these stats as signals, not goals.

Role prompt: Logic Editor (argument flow)

text
You are the logic editor for one survey subsection.

Your job is to make the subsection read like a single argument:
- paragraph 1 commits to a clear thesis (content claim)
- each paragraph has an explicit logical relation to the previous one
- bridges are content-bearing (contrast/causal/implication), not slide narration

Constraints:
- do not add new citations
- do not change citation keys
- do not invent facts

Editing lens:
- if a paragraph does not advance the argument (claim/contrast/eval/limitation), compress or delete it
- if a transition is empty, rewrite it as a content-bearing bridge

Inputs

  • sections/ (expects H3 body files like S<sec>_<sub>.md)
  • outline/subsection_briefs.jsonl (use thesis + paragraph_plan[].connector_phrase as intent)
  • Optional: outline/writer_context_packs.jsonl (preferred; has trimmed anchors/comparisons + must_use)

Outputs

  • output/SECTION_LOGIC_REPORT.md (PASS/FAIL for thesis; connector stats shown for diagnosis)

Manual / LLM-first (in place):

  • Update the H3 body files under sections/ (e.g., sections/S<sec>_<sub>.md) to fix thesis/bridges (no new citations; keep keys stable)

Workflow (self-loop)

  1. Run the checker script to surface the exact failing files.

  2. For each failing H3 file:

  • Work on the concrete H3 body file (pattern): sections/S<sec>_<sub>.md

  • Use outline/subsection_briefs.jsonl as the source of truth for the subsection thesis and paragraph-plan intent.

  • If available, prefer outline/writer_context_packs.jsonl to stay aligned with must_use anchors/constraints (no new cites).

  • Thesis (blocking)

    • Make paragraph 1 end with a conclusion-first thesis sentence.
    • Prefer a content claim, not meta narration. Avoid repetitive openers like This subsection argues/surveys ....
    • Minimal shape (3 sentences; paraphrase, don’t copy):
      1. claim / tension
      2. why it matters (protocol/evaluation relevance)
      3. how the subsection will resolve it (what contrasts/anchors it will use)
  • Flow (fix only when needed)

    • Add 1–2 short bridges where paragraphs feel disconnected.
    • Prefer subject-first sentences and mid-sentence glue (because/while/which) over paragraph-start adverbs.
    • Avoid PPT navigation (Next, we ..., We now turn to ...).
  1. Rerun the checker until output/SECTION_LOGIC_REPORT.md is PASS, then proceed to paragraph-curator, evaluation-anchor-checker, the final argument-selfloop snapshot, and merge.

Examples

Thesis signal (paragraph 1)

Bad (topic setup only):

  • Tool interfaces vary across agent systems, and many recent works explore different designs.

Better (conclusion-first claim):

  • A central tension in tool interfaces is balancing expressivity with verifiability; as a result, interface contracts often determine which evaluation claims transfer across environments.

Bad (meta narration):

  • This subsection argues that memory is important for agents.

Better (content claim):

  • Memory designs trade off retrieval reliability against write-time contamination, and this trade-off shows up as distinct failure modes under fixed evaluation protocols.

Bridges (avoid paragraph islands)

Bad (no relation):

  • X does ... (para 2)
  • Y does ... (para 3)

Better (explicit tie):

  • Whereas X optimizes for <axis>, Y shifts the bottleneck to <axis>; under fixed budgets, this changes whether the reported gains reflect better planning or simply more expensive search.

Done criteria

  • output/SECTION_LOGIC_REPORT.md shows - Status: PASS
  • No section file contains placeholders (TODO/…/...) or outline meta markers (Intent:/RQ:/Evidence needs:)
  • Every H3 has a clear paragraph-1 thesis; bridges are added only where flow is actually broken

Script

Quick Start

  • uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>

Notes:

  • The script is a checker; it does not rewrite prose.
  • Connector stats are printed for diagnosis. Do not “write to the counter”; write to the argument.

All Options

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

Examples

  • Default run:

    uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace>

  • Explicit output path (rare override; prefer defaults):

    uv run python .codex/skills/section-logic-polisher/scripts/run.py --workspace <workspace> --outputs output/SECTION_LOGIC_REPORT.md

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 Section Logic Polisher AI skill do?

Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging.

Why use Section Logic Polisher on TypingMind?

Because you install it once and use it with any model. Section Logic Polisher 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 Section Logic Polisher in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/section-logic-polisher. 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 Section Logic Polisher?

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 Section Logic Polisher?

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

Is the Section Logic Polisher 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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