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Agent Skill · 研究检索

evidence-selfloop(证据自循环)

evidence-selfloop 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-selfloop 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 46,825。

总安装 760周安装 32GitHub Stars 422下载 266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-selfloop(证据自循环)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/evidence-selfloop
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill evidence-selfloop
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-selfloop

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。

用于推动证据优先流水线收敛,避免撰写填充性文字,识别低支持子节问题根源。适用于 Codex、Claude、Cursor、Gemini CLI 中需要优化证据提取或映射覆盖率的场景。输出 actionable TODO 列表,指导重跑特定 skill 以解锁高质量写作。

需输入 subsection briefs 和 bindings 文件,建议提前校验路径与字段完整性。evidence-selfloop 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-selfloop

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-selfloop 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-selfloop 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-selfloop 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-selfloop 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-selfloop 用的人多吗?

公开数据是累计安装 760 次,近一周安装 32 次,下载 266 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-selfloop 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-selfloop

证据自循环 · 本页

760422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 Self-loop (C3/C4 fix → rebind → redraft)

Purpose: make the evidence-first pipeline converge without writing filler prose.

This skill reads the *intermediate evidence artifacts* (briefs/bindings/packs) and produces an actionable TODO list that answers:

  • Which subsections are under-supported?
  • Is the problem mapping/coverage (C2) or evidence extraction (C3) or binding/planning (C4)?
  • Which skill(s) should be rerun, in what order, to unblock high-quality writing?

Inputs

  • outline/subsection_briefs.jsonl
  • outline/evidence_bindings.jsonl (expects binding_gaps / binding_rationale if available)
  • outline/evidence_drafts.jsonl (expects blocking_missing, comparisons, eval protocol, limitations)
  • Optional (improves routing):

- outline/evidence_binding_report.md - outline/anchor_sheet.jsonl - papers/paper_notes.jsonl - papers/fulltext_index.jsonl - queries.md

Outputs

  • output/EVIDENCE_SELFLOOP_TODO.md (report-class; always written)

Self-loop contract (what “fixing evidence” means)

  • Prefer fixing upstream evidence, not writing around gaps.
  • If an evidence pack has blocking_missing, treat it as a STOP signal: strengthen notes/fulltext/mapping, then regenerate packs.
  • If bindings show binding_gaps, treat it as a ROUTING signal: either enrich the evidence bank for the mapped papers, expand mapping coverage, or adjust required_evidence_fields if unrealistic.

Recommended rerun chain (minimal):

  • If C3 evidence is thin: pdf-text-extractor → paper-notes → evidence-binder → evidence-draft → anchor-sheet → writer-context-pack
  • If C2 coverage is weak: section-mapper → outline-refiner → (then rerun C3/C4 evidence skills)

Workflow (analysis-only)

  1. Read queries.md (if present)
  • Use it only as a soft config hint (evidence_mode / draft_profile); do not override the artifact contract.
  1. Read outline/subsection_briefs.jsonl
  • For each sub_id, capture axes + required_evidence_fields (what evidence types this subsection expects).
  1. Read outline/evidence_bindings.jsonl
  • For each sub_id, surface binding_rationale and binding_gaps (what the binder could/could not cover from the evidence bank).
  1. (Optional) Read outline/evidence_binding_report.md
  • Use it as a human-readable summary; treat it as a view of outline/evidence_bindings.jsonl, not a separate truth source.
  1. Read outline/evidence_drafts.jsonl
  • Surface blocking_missing (STOP signals), and check for missing comparisons / eval protocol / limitations that would force hollow writing.
  1. (Optional) Read outline/anchor_sheet.jsonl
  • Check whether each subsection has at least a few citation-backed anchors (numbers / evaluation / limitations).
  1. (Optional) Read papers/paper_notes.jsonl and papers/fulltext_index.jsonl
  • Use these to route fixes: if evidence is abstract-only and missing eval tokens, prefer enriching notes/fulltext before drafting prose.

What the report contains

  • Summary counts: subsections with blocking_missing, with binding_gaps, and common failure reasons.
  • Per-subsection TODO: the smallest upstream fix path (skills + artifacts) to make the subsection writeable.

Status semantics (unblock rules)

This skill is the *prewrite router* for evidence quality. Treat its Status: line as the unblock contract:

  • PASS: no blocking_missing and no binding_gaps -> proceed to C5 writing (but still scan non-blocking writability smells: low comparisons/eval/anchors often predict hollow prose).
  • OK: no blocking_missing, but some binding_gaps -> you may draft, but expect weaker specificity; prefer fixing gaps first.
  • FAIL: missing inputs OR any blocking_missing -> do not write filler prose; fix upstream and rerun C3/C4.

Routing matrix (symptom -> root cause -> upstream fix)

Use this as a *semantic routing table* (not a script checklist). The goal is to fix the earliest broken intermediate artifact.

Symptom (where you see it)Likely root causeInspect firstSmallest upstream fix chain
evidence_drafts.blocking_missing: no usable citation keysmapped papers lack bibkey / bibkeys not in citations/ref.bibpapers/paper_notes.jsonl (bibkey fields), citations/ref.bibC3 paper-notes (ensure bibkeys) -> C4 citation-verifier -> rerun evidence-binder -> rerun evidence-draft
blocking_missing: title-only evidenceretrieval/metadata lacks abstracts (or aggressive filtering)papers/papers_raw.jsonl abstracts, papers/paper_notes.jsonl evidence_levelC1 literature-engineer (enrich metadata) OR C3 pdf-text-extractor (fulltext) -> rerun paper-notes
blocking_missing: no evidence snippets extractablenotes are too thin / evidence bank empty for mapped paperspapers/evidence_bank.jsonl (counts), papers/paper_notes.jsonlC3 paper-notes (richer extraction; prefer fulltext when possible) -> rerun C4 packs
blocking_missing: no concrete evaluation tokensnotes/bank did not extract benchmarks/metrics/budgetspapers/paper_notes.jsonl (metrics/benchmarks fields), outline/anchor_sheet.jsonlC3 paper-notes (extract eval anchors) -> rerun anchor-sheet + evidence-draft
evidence pack comparisons are sparse (signals: comparisons low)clusters are not contrastable OR mapping coverage too weakoutline/subsection_briefs.jsonl (clusters), outline/mapping.tsvC2 section-mapper (coverage) OR C3 subsection-briefs (better clusters) -> rerun evidence-draft
bindings.binding_gaps mentions benchmarks/metrics/protocolbinder cannot find evaluation-tagged evidence for this subsectionoutline/evidence_binding_report.md (tag mix), papers/evidence_bank.jsonl tagsC3 paper-notes (tag/evidence extraction) OR C2 expand mapping for that subsection -> rerun evidence-binder
binding_gaps mentions security/threat model/attacksmapped set lacks security-focused works or notes lack threat-model detailoutline/mapping.tsv, papers/paper_notes.jsonlC2 expand mapping (+ C1 queries if needed) OR C3 enrich notes -> rerun binder/packs
binding report looks mechanically uniform across H3 (same mix, low tag variance)binder selection too recipe-like OR evidence bank tags too coarseoutline/evidence_binding_report.md (tag mix), evidence bank tagstighten required_evidence_fields + improve evidence bank tags, then rerun binder; avoid writing around non-specific bindings

Interface with the writer self-loop (avoid writing around evidence)

  • If writer-selfloop is FAIL due to missing anchors/comparisons and the corresponding writer pack has pack_warnings, stop and run this evidence self-loop: the section is telling you the pack is not writeable.
  • Prefer fixing evidence gaps once, upstream, rather than patching every H3 with generic filler.

What this skill does NOT do

  • It does not edit papers/*, outline/*, or sections/*.
  • It does not invent new facts/citations.
  • It does not "relax" quality by changing thresholds; it routes you to the earliest artifact to fix.

Script

Quick Start

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

All Options

  • --workspace <dir>
  • --unit-id <U###> (optional)
  • --inputs <semicolon-separated> (optional override)
  • --outputs <semicolon-separated> (optional override; default writes output/EVIDENCE_SELFLOOP_TODO.md)
  • --checkpoint <C#> (optional)

Examples

  • Generate an evidence TODO list after C4 packs are generated:

- python.codex/skills/evidence-selfloop/scripts/run.py --workspace workspaces/<ws>

平台分布

Claude Code

27.4%
按下载量换算73

Gemini CLI

23.99%
按下载量换算64

Cursor

16.83%
按下载量换算45

Codex

12.74%
按下载量换算34

OpenCode

7.75%
按下载量换算21

Antigravity

3.66%
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

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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