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Agent Skill · 研究检索

evidence-binder(证据活页夹)

evidence-binder 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。

evidence-binder 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,权限为只读;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 48,626。

总安装 682周安装 29GitHub Stars 422下载 239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:evidence-binder(证据活页夹)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/evidence-binder
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill evidence-binder
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 evidence-binder

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。

用于将论文级证据池转换为可定位的子章节证据计划,提升写作质量与引用精准度。适用于 Codex、Claude、Cursor、Gemini CLI 中需要结构化引用证据的场景。通过绑定证据 ID 到当前子节,防止引用超纲或填充无依据内容。

需配合 outline 和 evidence bank 输入使用,建议提前验证文件路径与格式兼容性。evidence-binder 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/evidence-binder

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

    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

evidence-binder 怎么安装?

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

evidence-binder 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。

evidence-binder 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。

evidence-binder 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。

evidence-binder 用的人多吗?

公开数据是累计安装 682 次,近一周安装 29 次,下载 239 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。

evidence-binder 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
evidence-binder

证据活页夹 · 本页

682422只读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

Evidence Binder (NO PROSE)

Goal: convert a paper-level pool into a subsection-addressable evidence plan.

This skill is the bridge from “Evidence Bank” → “Writer”: the writer should only use evidence IDs bound to the current subsection.

Why this matters for writing quality:

  • Weak/undifferentiated bindings force the writer to either pad prose or cite out-of-scope.
  • Treat binding_gaps as a routing signal: fix upstream evidence/mapping instead of "writing around" missing evidence.

Inputs

  • outline/subsection_briefs.jsonl
  • outline/mapping.tsv
  • papers/evidence_bank.jsonl
  • Optional:

- citations/ref.bib (to validate cite keys when evidence items carry citations)

Outputs

  • outline/evidence_bindings.jsonl (1 JSONL record per subsection)
  • outline/evidence_binding_report.md (summary; bullets + small tables)

- Includes gaps (missing required evidence fields) and tag mix (selected evidence tags) so subsection-specific evidence needs are visible.

Output format (outline/evidence_bindings.jsonl)

JSONL (one object per H3 subsection). Best-effort fields (keep deterministic):

  • sub_id, title
  • paper_ids (papers in-scope for this subsection, from mapping.tsv)
  • mapped_bibkeys (bibkeys mapped to this subsection)
  • bibkeys (a selected subset to encourage subsection-first citations)
  • evidence_ids (selected evidence items from papers/evidence_bank.jsonl)
  • evidence_counts (small summary by claim_type / tag / evidence_level)
  • binding_rationale (short bullets; why the selected evidence covers this subsection’s axes / desired tags)
  • binding_gaps (list[str]; required evidence fields not covered by selected evidence; drives the evidence self-loop upstream)

A150++ density contract (default)

  • Use queries.md:per_subsection as the width contract (A150++ default: 28).
  • Bind enough evidence to make writing *concretely executable* without out-of-scope pressure:

- mapped_bibkeys: >= per_subsection - evidence_ids: >= per_subsection - 4 (A150++: >=24) - bibkeys (selected): >= 20 (so each H3 has a usable citation pool, not just a long mapped list)

Binding policy (how strict to be)

  • Subsection-first by default: the writer should primarily cite bibkeys and use evidence_ids bound to this sub_id.
  • Allow limited within-chapter reuse: citations from sibling H3s within the same H2 chapter may be reused for background/evaluation framing, but:

- keep >=3 subsection-specific citations per H3 (avoid “free cite drift”) - avoid cross-chapter reuse unless the outline explicitly calls for it

Workflow (NO PROSE)

  1. Read outline/subsection_briefs.jsonl to understand each H3’s scope/rq/axes.
  2. Read outline/mapping.tsv to know which papers belong to each subsection.
  3. Read papers/evidence_bank.jsonl and select a subsection-scoped set of evidence_id items per H3.
  4. If citations/ref.bib exists, sanity-check that any cite keys referenced by selected evidence items are defined.
  5. Write outline/evidence_bindings.jsonl and outline/evidence_binding_report.md.

Freeze policy

  • If outline/evidence_bindings.refined.ok exists, the script will not overwrite outline/evidence_bindings.jsonl.
  • Treat this marker as an explicit refinement/completion signal (especially in strict runs): only create it after you have checked binding_gaps and tag mix look subsection-specific.

Heterogeneity sanity check (avoid recipe-like bindings)

A common hidden failure mode is *mechanical uniformity*: every H3 ends up with the same claim_type/tag mix, which hides what each subsection is actually missing and pushes the writer toward generic prose.

Before you mark bindings as refined:

  • Scan outline/evidence_binding_report.md: different H3 should show meaningfully different tag mix / claim_type balance.
  • If most H3 look identical, treat it as a binder smell: tighten required_evidence_fields, adjust selection rationale, or enrich the evidence bank, then rerun.

Script

Quick Start

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

All Options

  • --workspace <dir>: workspace root
  • --unit-id <U###>: unit id (optional; for logs)
  • --inputs <semicolon-separated>: override inputs (rare; prefer defaults)
  • --outputs <semicolon-separated>: override outputs (rare; prefer defaults)
  • --checkpoint <C#>: checkpoint id (optional; for logs)

Examples

  • Bind evidence IDs after building the evidence bank:

- Ensure papers/evidence_bank.jsonl exists. - Run: python.codex/skills/evidence-binder/scripts/run.py --workspace workspaces/<ws>

Troubleshooting

Issue: some subsections have too few evidence IDs

Fix:

  • Strengthen papers/evidence_bank.jsonl via paper-notes (more extractable evidence items).
  • Or broaden the mapped paper set for the subsection via section-mapper, then rerun binder.

Issue: binding_gaps is non-empty (missing evidence types)

What it means:

  • The subsection brief requires certain evidence fields (e.g., benchmarks/metrics/security/tooling), but the bound evidence items do not cover them.

Fix (self-loop upstream):

  • Prefer enriching papers/evidence_bank.jsonl / papers/paper_notes.jsonl for mapped papers (extract benchmark/metric/failure-mode details).
  • If the mapping is weak for that evidence type, expand outline/mapping.tsv for the subsection and rerun binder.
  • If the requirement is unrealistic for the subsection’s scope, revise outline/subsection_briefs.jsonl:required_evidence_fields and rerun binder.

平台分布

Claude Code

29.58%
按下载量换算71

Gemini CLI

24.68%
按下载量换算59

Cursor

17.35%
按下载量换算41

Codex

12.78%
按下载量换算31

OpenCode

8.31%
按下载量换算20

Antigravity

3.63%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。

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