Token导航 LogoToken导航TokenDH.com
研究检索权限需确认github未标认证来源可访问clear审计通过

global-reviewer全球审稿人

Agent Skill

global-reviewer 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。

总安装

838

周安装

36

GitHub Stars

422

下载量

294
CodexClaudeCursorGemini CLI

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:global-reviewer(全球审稿人)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/global-reviewer
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill global-reviewer
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 global-reviewer

简介

global-reviewer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 适用于研究检索类任务,可结合来源仓库和原始 README 核验具体用法。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和联网需求。
  • 建议在使用前检查维护状态,避免触发不必要的文件读写或命令执行操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Global Reviewer (survey draft)

Purpose: make the draft read like a coherent paper (not stitched subsections) and make problems auditable.

Role cards (use explicitly)

Consistency Reviewer (auditor)

Mission: find cross-section issues a real reviewer would flag, and route them to the right upstream fix.

Do:

  • Check scope/taxonomy consistency and terminology drift across chapters.
  • Flag underspecified claims (numbers without task/metric/constraint).
  • Treat missing evidence as a failure signal; route upstream.

Avoid:

  • Writing around gaps by adding new claims or citations.

Coherence Editor (bridge finder)

Mission: spot stitched-island structure and front-matter weaknesses that cause it.

Do:

  • Identify where transitions/leads are doing planner talk instead of argument bridges.
  • Flag repeated evidence-policy disclaimers and point to front matter as the single home.

Avoid:

  • Style-only nitpicks that do not change readability or verifiability.

Role prompt: Consistency Reviewer (AI paper reviewer mindset)

You are a meticulous reviewer for a survey manuscript.

Your job is to surface cross-section problems that would matter to a real reader/reviewer:
- missing or underspecified evidence for claims
- scope drift and taxonomy inconsistency
- weak front matter (boundary/methodology not stated, so H3s carry repeated disclaimers)
- stitched-island structure (no argument chain across sections)

Constraints:
- do not invent facts or citations
- do not add new citation keys
- treat missing evidence as a failure signal: route upstream instead of writing around it

Output style:
- bullets-first
- actionable, route-to-skill recommendations

This is not “polish for style”. It is a contract check:

  • do claims align to evidence/citations?
  • do sections connect via a consistent lens?
  • does the front matter set the boundary and methodology so H3s can stay content-focused?

Inputs

  • output/DRAFT.md
  • Context (read-only; used to avoid drift):

- outline/outline.yml - outline/taxonomy.yml - outline/mapping.tsv - outline/claim_evidence_matrix.md - citations/ref.bib

Outputs

  • output/GLOBAL_REVIEW.md (bullets-first report; always written)
  • output/DRAFT.md (optional safe edits; only when edits are low-risk)

Non-negotiables

  • No invented facts.
  • No invented citations.
  • Do not add/remove citation keys.
  • Missing evidence is a failure signal: write TODOs and route upstream; do not “write around” gaps.

What this skill owns (and what it does not)

Owns:

  • Cross-section coherence (throughline, definitions, scope)
  • Paper voice integrity (remove planner/pipeline narration where safe)
  • Terminology consistency (canonical term + synonym policy)
  • Claim→evidence hygiene (underspecified numbers, weak citations)

Does not own:

  • Changing the outline structure (route to C2)
  • Adding new sources/citations (route to C1/C4)
  • Strengthening missing evaluation details when notes are thin (route to C3/C4)

Workflow (use the context files explicitly)

  1. Check structure against outline/outline.yml
  • Verify the draft’s major sections and subsection order matches the intended ToC.
  • Identify which H2 is Introduction/Related Work so you can evaluate front-matter duties.
  1. Check scope vocabulary against outline/taxonomy.yml
  • Verify node descriptions and boundaries are consistent with how the draft uses the terms.
  • Flag mixed axes without a rule (model family vs capability vs evaluation).
  1. Check coverage signals via outline/mapping.tsv
  • Spot chapters/subsections that are under-mapped (likely under-cited or hollow).
  • Flag over-reuse of the same papers across many sections (suggests brittle synthesis).
  1. Spot-check claims using outline/claim_evidence_matrix.md
  • Sample 5–10 claims and verify each has plausible evidence fields and citations in the draft.
  • If the matrix is thin or mismatched, route upstream (C3/C4) instead of polishing prose.
  1. Sanity-check citation keys against citations/ref.bib
  • Flag undefined keys or suspicious naming (e.g., “GPT-5”) unless the cited work uses that label.

Report format (required)

output/GLOBAL_REVIEW.md must be bullets-first and contain these headings verbatim (so gates can verify them):

  • ## A. Input integrity / placeholder leakage
  • ## B. Narrative and argument chain
  • ## C. Scope and taxonomy consistency
  • ## D. Citations and verifiability (claim -> evidence)
  • ## E. Tables and structural outputs

Include a top line:

  • - Status: PASS (or - Status: OK) only after all blocking issues are addressed.

What to check (high-value, paper-like)

A. Input integrity / placeholder leakage

Look for:

  • leaked scaffolds (, TODO, “enumerate 2-4 …”, “scope/design space/evaluation practice”)
  • planner talk in transitions or section openers
  • repeated evidence-policy boilerplate inside H3s

Action:

  • If placeholders exist: block and route upstream (do not patch them with “generic prose”).
  • If evidence-policy disclaimer repeats across H3s: move/keep it once in front matter and delete repeats.

B. Narrative and argument chain

Goal: every section does an argument move.

Check:

  • H2 throughline: Introduction defines the boundary and evaluation lens; chapters execute comparisons; Discussion synthesizes cross-cutting risks/gaps.
  • H3 “argument shape”: tension → contrast → evaluation anchor → synthesis → limitation.
  • “Generator voice”: narration templates (This subsection...) and slide navigation (Next, we...).

Action (safe edits allowed):

  • Replace navigation sentences with argument bridges (no new facts).

Bad:

  • Next, we move from planning to memory.

Better:

  • Planning specifies how decisions are made; memory determines what information those decisions can reliably condition on under a fixed protocol.

C. Scope and taxonomy consistency

Check:

  • Scope boundary is explicit and consistent (what counts as an “agent” here; what does not).
  • Taxonomy nodes match the paper’s claims (no mixed axes without a rule).
  • No silent drift (e.g., includes lots of multi-agent safety papers when scope is tool-use agents).

Action:

  • If scope drift is structural: route to C2 (tighten outline + mapping).
  • If scope drift is minor: tighten one scope sentence in the front matter (no new citations).

D. Citations and verifiability (claim -> evidence)

Write a small claim-evidence table (5–10 rows):

  • claim | section | citations | evidence_field | evidence_level

Flag:

  • cite dumps and paragraphs with weak/irrelevant citations
  • underspecified quantitative claims (numbers without task/metric/constraint context)
  • ambiguous model naming (e.g., “GPT-5”) unless the cited paper uses that label

Action:

  • If you can clarify context without new facts (e.g., “under a fixed budget/tool access”), do so.
  • Otherwise: mark as TODO and route to C3/C4 (paper notes / evidence packs).

E. Tables and structural outputs

Check:

  • Tables answer a concrete comparison question (schema), not copied outline bullets.
  • Rows contain citations.

Action:

  • If tables are intermediate-only in this pipeline run: ensure the draft does not contain thin “table placeholder” chapters.

Recommended fix order (routing)

When the report finds issues, recommend the smallest fix path:

  • Placeholder leakage / thin packs -> C3/C4 (paper-notesevidence-draftanchor-sheetwriter-context-pack)
  • Section voice/template problems -> C5 local rewrite (writer-selfloop / subsection-polisher / draft-polisher)
  • Citation scope drift -> C2/C4 (section-mapper / evidence-binder) then rewrite the affected sections
  • Global unique citations too low -> citation-diversifiercitation-injector (then draft-polisher)

Safe edits allowed (optional)

If and only if edits are low-risk and do not change citation keys:

  • unify terminology
  • remove slide-like narration and planner talk
  • add 1–2 short argument-bridging transitions between major sections
  • tighten scope statements and conclusion closure

Script

This skill includes a deterministic helper script that generates a gate-compliant output/GLOBAL_REVIEW.md from the current draft and context (no invented facts/citations).

Quick Start

  • python.codex/skills/global-reviewer/scripts/run.py --help
  • python.codex/skills/global-reviewer/scripts/run.py --workspace workspaces/<ws>

All Options

  • --workspace <dir>
  • --unit-id <U###> (optional; for logs)
  • --inputs <semicolon-separated> (rare override; prefer defaults)
  • --outputs <semicolon-separated> (rare override; default writes output/GLOBAL_REVIEW.md)
  • --checkpoint <C#> (optional)

Examples

  • Generate a global review after merging a draft:

- python.codex/skills/global-reviewer/scripts/run.py --workspace workspaces/<ws>

Freeze policy:

  • If you hand-edit the review and want to freeze it, create output/GLOBAL_REVIEW.refined.ok to prevent overwrites.

Notes:

  • The script does not “write” new survey content; it summarizes integrity/citation/structure signals and re-runs draft quality checks.

Troubleshooting

Issue: review flags missing citations / undefined keys

Fix:

  • Run citation-verifier and ensure citations/ref.bib contains every cited key in output/DRAFT.md.

Issue: review suggests changes that would add new claims

Fix:

  • Convert those into “missing evidence” TODOs instead; this pass must not invent facts or citations.

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

26.49%
按下载量换算78

Gemini CLI

23.26%
按下载量换算68

Cursor

16.89%
按下载量换算50

Codex

11.87%
按下载量换算35

OpenCode

8.55%
按下载量换算25

Antigravity

3.24%
按下载量换算10

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

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。

来源信息

继续浏览同类 Skills