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Subsection Polisher

Community
WILLOSCAR
subsection-polisher

Polish a single H3 unit file under `sections/` into survey-grade prose (de-template + contrast/eval/limitation), without changing citation keys.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill namesubsection-polisher
Stars
513
Forks
39
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Subsection 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/subsection-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/subsection-polisher .claude/skills/subsection-polisher
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Subsection 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 Subsection 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 Subsection 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.

Subsection Polisher (local, pre-merge)

Triggers & routing

  • Trigger: subsection polisher, per-subsection polish, polish section file, 小节润色, 去模板, 结构化段落.
  • Use when: sections/S*.md exists but reads rigid/template-y; you want to fix quality locally before section-merger.

Purpose: upgrade one sections/S<sub_id>.md (H3 body-only) so it reads like survey prose before you merge into output/DRAFT.md.

This is intentionally local: fix one unit at a time, rerun gates, and converge without rewriting the whole paper.

Role cards (use explicitly)

Local Section Editor

Mission: improve one H3’s argument density and paper voice without changing citation keys.

Do:

  • Rewrite the opener as tension -> why it matters -> thesis (end paragraph 1 with thesis).
  • Add explicit contrasts and one evaluation anchor when missing.
  • Add a subsection-specific limitation that changes interpretation.

Avoid:

  • Adding/removing citation keys or moving citations across subsections.
  • Replacing content with generic boilerplate.

Evidence Steward (stop padding)

Mission: prevent polishing from turning into invention when evidence is thin.

Do:

  • If you cannot write a contrast or evaluation anchor without guessing, stop and route upstream.

Avoid:

  • Strengthening claims beyond what the existing citations can support.

Role prompt: Local Section Editor (one H3 at a time)

text
You are editing one survey subsection to make it read like paper prose.

Your goal is to remove generator voice and strengthen argument moves without changing citation keys:
- opener: tension + why-it-matters + thesis (no narration)
- add explicit contrasts and an evaluation anchor if missing
- make at least one cross-paper synthesis paragraph (>=2 citations)
- add a subsection-specific limitation (not boilerplate)

Constraints:
- do not add/remove citation keys
- do not invent facts
- keep scope local to this H3

Inputs

  • Target file: sections/S<sub_id>.md (H3 body-only)
  • Preferred context: outline/writer_context_packs.jsonl
  • Fallback context: outline/subsection_briefs.jsonl + outline/evidence_drafts.jsonl
  • citations/ref.bib

Output

  • Updated sections/S<sub_id>.md (same path; citation keys unchanged)

Non-negotiables (contract)

  • Citation keys are immutable: do not add/remove any [@BibKey] markers.
  • Scope is immutable: keep all citations within this H3’s allowed scope (outline/evidence_bindings.jsonl / writer pack allowed_bibkeys_*).
  • No invented facts: if you cannot write a concrete contrast or evaluation anchor without guessing, stop and fix upstream evidence.
  • Body-only: no headings; section-merger adds headings.

Target quality (what “polished” means)

A polished H3 reads like an argument, not a topic list:

  • Paragraph 1 ends with a thesis (conclusion-first takeaway) and does not use narration templates.
  • At least two explicit contrasts (A vs B) using contrast words.
  • At least one evaluation anchor paragraph (task/benchmark + metric + constraint/budget/tool access when relevant).
  • At least one cross-paper synthesis paragraph with >=2 citations in the same paragraph.
  • At least one limitation/caveat tied to protocol mismatch / missing details / unclear threat model (not boilerplate).

Paper voice constraints (high signal anti-patterns)

Delete / rewrite these (they read like a generator):

  • Outline narration: This subsection ..., In this subsection, we ....
  • Slide navigation: Next, we move ..., We now turn to ..., In the next section ....
  • Meta guidance: survey synthesis/comparisons should ....
  • Evidence-policy disclaimer spam: repeated abstract-only/title-only/provisional boilerplate inside H3.
  • Count-based slot openers: repeated "Two limitations..." / "Three takeaways..." used as paragraph starters.

Prefer these (paper voice):

  • Content-first openers: A central tension is ..., In practice, ..., One recurring pattern is ....
  • Argument bridges (not navigation): This contrast matters because ..., These assumptions shape ....
  • Embedded citations as evidence (no trailing dump tags).

Workflow (one subsection)

  1. Load the subsection contract
  • Read this subsection’s pack in outline/writer_context_packs.jsonl.
  • If the pack is missing/thin, fall back to outline/subsection_briefs.jsonl (thesis/tension/paragraph_plan) + outline/evidence_drafts.jsonl (comparisons/eval/limitations).
  • Extract (write down, not in the prose):
    • tension_statement + thesis
    • 2–3 comparison_cards you will use for A-vs-B contrasts
    • 1 evaluation_anchor_minimal (task/metric/constraint)
    • 1 limitation hook from the evidence pack
  1. Preflight (kept out of the final prose)
  • Draft 4 one-line sentences:
    • Tension
    • Contrast (A vs B; >=2 citations)
    • Evaluation anchor (task/metric/constraint)
    • Limitation If you cannot write these without guessing, stop and push the gap upstream (paper-notes / evidence-draft).
  1. Opener rewrite (paragraph 1)
  • Remove narration openers.
  • Write: 1–2 sentences tension/decision/lens + 1 sentence why it matters + end with the thesis.

Bad:

  • This subsection surveys tool interfaces for agents.

Better:

  • A central tension in tool interfaces is balancing expressive action spaces with verifiable execution; interface contracts largely determine which evaluation claims are meaningful.
  1. Paragraph pass (argument moves > listing)
  • Rewrite paragraph-by-paragraph using the grad-paragraph micro-structure:
  • Best-of-2 rewrite (recommended): when a paragraph feels slot-like, draft 2 candidate rewrites and keep the one with clearer argument move + less template cadence (citations unchanged).
    • tension → contrast → evaluation anchor → limitation
  • Ensure you include:
    • =2 explicit contrasts (not “A then B” summaries)

    • =1 evaluation anchor paragraph

    • =1 cross-paper synthesis paragraph (>=2 citations)

    • =1 limitation paragraph/clause that is subsection-specific

  1. Citation embedding pass (no dumps)
  • Rewrite paragraphs where citations appear only at the end as [@a; @b; @c].
  • Ensure every citation key you keep is defined in citations/ref.bib.

Bad (dump):

  • Many systems adopt tool schemas. [@a; @b; @c]

Better (cite-as-evidence):

  • Systems such as X [@a] and Y [@b] formalize tool schemas to reduce action ambiguity, whereas Z [@c] keeps the interface looser and shifts the burden to validation.
  1. Rhythm + de-template pass
  • Vary paragraph openings; avoid repeating the same synthesis stem across many paragraphs (especially Taken together).
  • Delete empty glue sentences that don’t add a claim, contrast, protocol detail, or limitation.
  1. Recheck (do not skip)
  • Run section-logic-polisher and address FAILs (thesis + connector density) without changing citation keys.
  • Rerun writer-selfloop (or the strict quality gate) and fix only what the report flags.

Rewrite recipes (common failure -> fix)

Use these as rewrite intentions, not copy-paste templates.

  1. Narration opener -> content claim
  • This subsection surveys ... -> A central tension is ...; this matters because ... (end paragraph 1 with the thesis).
  1. Slide navigation -> argument bridge
  • Next, we move from planning to memory. -> Planning specifies how decisions are made; memory determines what information those decisions can reliably condition on under a fixed protocol.
  1. Disclaimer spam -> one policy paragraph + local caveat only
  • Delete repeated abstract-only evidence boilerplate.
  • Keep evidence policy once in Intro/Related Work; in H3, only add a local caveat when it changes the interpretation of a specific comparison.
  1. Meta “survey should” -> literature-facing observation
  • Therefore, survey comparisons should control for tool access. -> Across reported protocols, tool access and budget assumptions vary widely, making head-to-head comparison fragile unless those constraints are normalized.
  1. Too-vague quantitative claim -> add minimal context (or weaken)
  • If a paragraph keeps a number, add: task type / metric definition / constraint (budget/cost/tool access) in the same paragraph and keep the citation embedded.
  • If the context is unknown from the evidence pack, rewrite the claim as qualitative and mark the missing field as a verification target (without boilerplate).

Stop conditions (when polishing is the wrong move)

Stop and go upstream if:

  • you cannot write a contrast without guessing (evidence pack is title/abstract-only)
  • the subsection lacks evaluation anchors (no benchmarks/metrics/protocol details in notes)
  • you keep needing out-of-scope citations to make the argument work

Acceptance checklist

  • Paragraph 1 ends with a thesis (no narration templates).
  • >=2 explicit contrasts, >=1 evaluation anchor, >=1 synthesis paragraph (>=2 citations), >=1 limitation.
  • No slide-like navigation / meta guidance / repeated evidence-policy boilerplate.
  • No citation-dump paragraphs; citations are embedded in claim sentences.
  • section-logic-polisher and writer-selfloop no longer flag this file.

Frequently asked questions

What does the Subsection Polisher AI skill do?

Polish a single H3 unit file under `sections/` into survey-grade prose (de-template + contrast/eval/limitation), without changing citation keys.

Why use Subsection Polisher on TypingMind?

Because you install it once and use it with any model. Subsection 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 Subsection 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/subsection-polisher. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Subsection 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 Subsection Polisher?

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

Is the Subsection 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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