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Agent Skill · 研究检索

subsection-polisher(分段抛光机)

subsection-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。

subsection-polisher 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 43,020。

总安装 1,022周安装 43GitHub Stars 422下载 358核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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:subsection-polisher(分段抛光机)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/subsection-polisher
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill subsection-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 subsection-polisher

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找和检索相关信息,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。

当前顶部介绍:subsection-polisher 用于查找、检索和筛选相关信息。当前底部简介为空,暂无补充说明。

来源仓库内路径:skills/subsection-polisher

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/willoscar/research-units-pipeline-skills --ski…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

subsection-polisher 怎么安装?

在终端执行 npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill subsect…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

subsection-polisher 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

subsection-polisher 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

subsection-polisher 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

subsection-polisher 用的人多吗?

公开数据是累计安装 1,022 次,近一周安装 43 次,下载 358 次,GitHub 422 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

subsection-polisher 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
subsection-polisher

分段抛光机 · 本页

1,022422权限未标注MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Subsection Polisher (local, pre-merge)

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)

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.

平台分布

Claude Code

29.34%
按下载量换算105

Gemini CLI

22.53%
按下载量换算81

Cursor

17.11%
按下载量换算61

OpenCode

11.41%
按下载量换算41

Codex

6.51%
按下载量换算23

trae

3.44%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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