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section-logic-polisher部分逻辑抛光器

Agent Skill

section-logic-polisher 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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1,175

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:section-logic-polisher(部分逻辑抛光器)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/section-logic-polisher
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill section-logic-polisher
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill section-logic-polisher

简介

用于优化信息结构或逻辑表达,提升内容组织与可读性。

  • 适合在撰写报告、总结或提炼复杂信息时辅助梳理思路与层次。
  • 通过 GitHub 安装并使用 npx 命令调用,输入文本后返回结构化建议。
  • 输出为建议性质,实际修改应由用户确认,避免过度干预原始内容。
  • section-logic-polisher 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Section Logic Polisher (thesis + argument bridges)

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 redundancy/overgrowth (sections only get longer), use paragraph-curator for a select->fuse pass. This skill stays focused on thesis + bridges.

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)

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 transition-weaver and section-merger.

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

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

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: python.codex/skills/section-logic-polisher/scripts/run.py --workspace workspaces/<ws>
  • Explicit output path (rare override; prefer defaults): python.codex/skills/section-logic-polisher/scripts/run.py --workspace workspaces/<ws> --outputs output/SECTION_LOGIC_REPORT.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.26%
按下载量换算106

Gemini CLI

25.63%
按下载量换算96

Cursor

16.46%
按下载量换算62

Codex

12.14%
按下载量换算46

trae

7.97%
按下载量换算30

OpenCode

3.85%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

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来源信息

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