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Agent Skill · 研究检索

awl-academic-rewriter(awl 学术重写者)

awl-academic-rewriter 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

awl-academic-rewriter 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,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 29,915。

总安装 2,970周安装 125GitHub Stars 公开资料未说明下载 1,040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-0
版本
1.0.0

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:awl-academic-rewriter(awl 学术重写者)
来源仓库:https://github.com/junwugit/awl-academic-rewriter
安装命令:
openclaw skills install awl-academic-rewriter
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install awl-academic-rewriter

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、检索和筛选相关信息,适合在 OpenClaw 中快速定位候选结果。支持基于关键词、任务场景或来源线索进行信息匹配与过滤。可通过安装命令部署,建议结合原始 README 核验具体用法。安装前需确认权限范围、维护状态及是否涉及联网或文件操作。

awl-academic-rewriter 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 OpenClaw;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 openclaw skills install awl-academic-rewriter;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

awl-academic-rewriter 怎么安装?

在终端执行 openclaw skills install awl-academic-rewriter。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

awl-academic-rewriter 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

awl-academic-rewriter 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

awl-academic-rewriter 是免费的吗?

来源标注的许可证是 MIT-0。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。

awl-academic-rewriter 用的人多吗?

公开数据是累计安装 2,970 次,近一周安装 125 次,下载 1,040 次。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。

awl-academic-rewriter 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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 OpenClaw,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
awl-academic-rewriter

awl 学术重写者 · 本页

2,970—只读MIT-0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

name
awl-academic-rewriter
description
Rewrite English prose sentence by sentence into a more academic version using the bundled AWL/NAWL vocabulary list. Use when the user asks to academicize, polish, revise, or rewrite English text with academic vocabulary while preserving meaning, tense, claims, and document structure, and when two Markdown outputs are needed: a sentence-level change report and a complete revised document.

AWL Academic Rewriter

Purpose

Use the first-column AWL/NAWL headword list in references/awl-headwords.txt to revise English text into a more academic register. Let the model judge, sentence by sentence, which ordinary or imprecise expressions can be replaced by suitable academic vocabulary. Preserve the original meaning, tense, aspect, modality, negation, certainty level, evidence strength, and document structure unless a grammar correction requires a local change.

Resources

  • references/awl-headwords.txt: default vocabulary source; one first-column AWL/NAWL headword per line.
  • references/awl.csv: full source table for selected headwords only. Columns are Word, Derivatives, and English Definition.
  • scripts/awl_lookup.py: headword helper for listing, filtering, verifying first-column membership, retrieving all three CSV columns for selected headwords, and detecting headwords already present in input text. It does not score semantic replacement candidates.

Use the helper from the skill directory, for example:

python3 scripts/awl_lookup.py --all --limit 40
python3 scripts/awl_lookup.py --contains analy
python3 scripts/awl_lookup.py --word analysis --word analyze
python3 scripts/awl_lookup.py --details analysis --details factor
python3 scripts/awl_lookup.py --text-file input.txt

Required Output Files

Always save two Markdown files unless the user explicitly asks for a different format:

  1. Change report: if the input has a filename, use <stem>-academic-changes.md; otherwise use academic-revision-changes.md.
  2. Complete revised document: if the input has a filename, use <stem>-academic-revised.md; otherwise use academic-revised-document.md.

The change report must contain a sentence-by-sentence table with these columns:

| # | Original sentence | Revised sentence | AWL/NAWL terms used | Modification method |
|---|---|---|---|---|

In Modification method, state concrete edits, such as vocabulary substitution, grammar correction, clause restructuring, hedging, nominalization, or cohesion improvement. Keep explanations concise and do not invent reasons not visible in the text.

The complete revised document must contain only the revised text, preserving the original headings, paragraph order, list structure, citations, quoted material, numbers, and formatting as much as possible.

Workflow

  1. Read the input text from the user's message or file. If the user gives multiple files, process each file separately unless they ask for a merged output.
  2. Preserve structural units first: headings, paragraphs, lists, tables, citations, code blocks, formulas, references, and quoted passages.
  3. Split prose into sentences within each paragraph. Avoid splitting inside common abbreviations, decimal numbers, initials, citations, or parenthetical references.
  4. Identify sentence meaning and rhetorical function before rewriting. Do not replace words merely because an academic synonym exists.
  5. Use references/awl-headwords.txt as the candidate vocabulary set. Do not rely on automatic synonym scoring. For each sentence, let the model decide whether any source word or phrase can be replaced by a headword from the list without changing meaning.
  6. After selecting candidate headwords, query references/awl.csv for those specific headwords and retrieve all three columns. Prefer python3 scripts/awl_lookup.py --details <headword> instead of loading the full CSV.
  7. Use the retrieved Derivatives and English Definition to confirm semantic fit and select the correct grammatical form.
  8. Rewrite each sentence according to the constraints below.
  9. Reassemble the revised document in the original order and write both required Markdown files.
  10. Run a final consistency pass comparing original and revised sentences for meaning, tense, factual claims, entity names, citations, and formatting.

Headword-Based Replacement Method

For each sentence:

  1. Identify the sentence's core proposition, tense, stance, and logical relations.
  2. Identify ordinary, vague, conversational, or grammatically weak expressions that could be made more academic.
  3. Consult references/awl-headwords.txt and select tentative headwords that may express the intended meaning in context.
  4. Query references/awl.csv for each tentative headword and inspect all three columns: Word, Derivatives, and English Definition.
  5. Confirm the definition fits the sentence meaning. Use a listed derivative when it is the best grammatical form; otherwise use an inflected form of the headword only if it remains semantically faithful.
  6. Record the first-column headword in the change report even when the revised sentence uses a derivative.
  7. Do not replace a word if the retrieved definition or derivative list suggests the headword would make the sentence less precise, more inflated, or semantically different.

This skill deliberately places semantic judgment on the model rather than on scripts/awl_lookup.py. The script is only a compact vocabulary access and verification tool.

Rewriting Constraints

  • Preserve the source meaning and factual scope. Do not add claims, evidence, citations, statistics, causal relationships, or certainty that the source does not contain.
  • Preserve tense, aspect, modality, polarity, and degree. For example, do not turn "may reduce" into "reduces" or "did not find" into "found".
  • Replace only vocabulary that improves academic precision, formality, or cohesion. It is acceptable for a sentence to receive no AWL/NAWL substitution if substitution would distort meaning.
  • Introduce academic vocabulary only when its first-column headword appears in references/awl-headwords.txt and its full references/awl.csv row has been checked. Use a grammatical derivative or inflected form when necessary, but list the base headword in AWL/NAWL terms used.
  • Correct grammar, punctuation, article use, agreement, and awkward syntax when needed.
  • Prefer concise academic phrasing over inflated prose. Avoid overloading a sentence with multiple academic terms when one precise substitution is enough.
  • Preserve technical terms, proper nouns, named methods, discipline-specific terms, citations, quoted text, numbers, units, and defined labels unless they contain clear grammar errors outside the quoted or fixed material.
  • Keep paragraph structure and logical transitions stable. Add or adjust transitions only when they clarify an existing relationship.
  • If the input includes non-prose elements such as code, equations, reference lists, or raw data, preserve them unchanged unless the user explicitly asks to revise them.

Quality Checks

Before finalizing, verify that:

  • Every original prose sentence has one corresponding revised sentence in the change report.
  • The complete revised document contains the same substantive content and order as the original.
  • Each listed AWL/NAWL term maps to a first-column headword in references/awl-headwords.txt.
  • Each selected headword has been checked against its full references/awl.csv row before final use.
  • Grammar fixes do not change meaning or tense.
  • The revised document reads as coherent academic English without sounding artificially synonym-substituted.

平台分布

OpenClaw

86.92%
按下载量换算904

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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