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evidence-binder证据活页夹

Agent Skill

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。

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682

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422

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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: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

简介

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。

SKILL.md

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.

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

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

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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

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来源信息

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