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evidence-draft证据草稿

Agent Skill

evidence-draft 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

742

周安装

30

GitHub Stars

422

下载量

233
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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-draft(证据草稿)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/evidence-draft
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill evidence-draft
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-draft

简介

用于从 briefs、笔记和可选证据绑定中构建确定性 outline/evidence_drafts.jsonl 文件。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中需要自动化生成草稿内容的开发流程。
  • 支持稀疏证据路由与评估锚点规则,确保内容强度与证据支撑匹配。
  • 建议在使用前确认 references 目录结构,并检查 JSONL 格式是否符合预期契约。
  • evidence-draft 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 Draft

Build deterministic outline/evidence_drafts.jsonl packs from briefs + notes + optional evidence bindings.

Compatibility mode is active: this migration preserves the existing JSONL contract while moving evidence-quality policy, sparse-evidence routing, and evaluation-anchor rules into references/ and assets/.

Load Order

Always read:

  • references/overview.md
  • references/evidence_quality_policy.md

Read by task:

  • references/block_vs_downgrade.md when deciding whether thin evidence should block drafting or only downgrade claim strength
  • references/evaluation_anchor_rules.md when evaluation tokens, protocol context, or numeric claims are weak
  • references/examples_sparse_evidence.md for evidence-thin pack calibration
  • references/source_text_hygiene.md when paper self-narration or generic result wrappers are leaking into pack snippets / claim candidates

Machine-readable assets:

  • assets/evidence_pack_schema.json
  • assets/evidence_policy.json
  • assets/source_text_hygiene.json

Inputs

Required:

  • outline/subsection_briefs.jsonl
  • papers/paper_notes.jsonl
  • citations/ref.bib

Optional but recommended:

  • papers/evidence_bank.jsonl
  • outline/evidence_bindings.jsonl

Outputs

Keep the current output contract:

  • outline/evidence_drafts.jsonl
  • optional human-readable mirrors under outline/evidence_drafts/

Script Boundary

Use scripts/run.py only for:

  • deterministic joins across briefs / notes / evidence bank / bindings
  • snippet extraction and provenance assembly
  • policy-driven blocking_missing / downgrade_signals / verify_fields materialization
  • pack validation and Markdown mirror generation

Do not treat run.py as the place for:

  • filler bullets that make thin evidence look complete
  • hidden sparse-evidence judgment that is not inspectable from references/ / assets/
  • reader-facing narrative prose

Output Shape Rules

Keep these stable:

  • preserve the existing top-level pack fields already used by downstream survey pipelines
  • claim_candidates must remain snippet-derived
  • concrete_comparisons must remain genuinely two-sided; if one cluster has no usable highlight, drop the card and surface thin evidence upstream instead of fabricating an A-vs-B contrast
  • snippet sampling should stay cluster-aware: when a subsection has explicit clusters, evidence selection should avoid collapsing onto one route just because its abstracts contain louder result sentences
  • sparse evidence should surface as explicit blockers / downgrade signals / verify fields, not filler bullets
  • citation keys must remain constrained to citations/ref.bib

Compatibility Notes

Current mode is reference-first with deterministic compatibility:

  • assets/evidence_policy.json defines pack thresholds and sparse-evidence routing
  • assets/evidence_pack_schema.json documents/validates the stable pack shape
  • scripts/run.py still materializes the existing JSONL + Markdown outputs, but no longer pads sparse sections with generic caution prose

Quick Start

  • python.codex/skills/evidence-draft/scripts/run.py --workspace <workspace_dir>

Execution Notes

When running in compatibility mode, scripts/run.py currently reads:

  • outline/subsection_briefs.jsonl
  • papers/paper_notes.jsonl
  • citations/ref.bib
  • optionally papers/evidence_bank.jsonl and outline/evidence_bindings.jsonl
  • assets/evidence_policy.json and assets/evidence_pack_schema.json

Script

Quick Start

  • python.codex/skills/evidence-draft/scripts/run.py --workspace <workspace_dir>

All Options

  • --workspace <dir>
  • --unit-id <id>
  • --inputs <path1;path2>
  • --outputs <path1;path2>
  • --checkpoint <C*>

Examples

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

Troubleshooting

  • If packs look complete despite thin evidence, inspect assets/evidence_policy.json and references/block_vs_downgrade.md before changing Python.
  • If evaluation bullets are generic, inspect references/evaluation_anchor_rules.md and the policy asset.
  • If claims are strong but evidence is abstract/title-only, downgrade via downgrade_signals and verify_fields rather than adding narrative caveats.

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

26.52%
按下载量换算62

Gemini CLI

20.6%
按下载量换算48

Cursor

18.65%
按下载量换算43

OpenCode

12.4%
按下载量换算29

Codex

6.95%
按下载量换算16

Antigravity

3%
按下载量换算7

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。

来源信息

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