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outline-refiner轮廓细化器

Agent Skill

outline-refiner 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

734

周安装

30

GitHub Stars

422

下载量

235
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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:outline-refiner(轮廓细化器)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/outline-refiner
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill outline-refiner
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 outline-refiner

简介

outline-refiner 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。

  • 适用于大纲优化、细节补充或结构精简等场景。
  • 通过关键词匹配返回优化策略、删减建议或增强方向。
  • 安装命令为 npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill outline-refiner。
  • 使用前请确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。

SKILL.md

Outline Refiner (Planner pass, NO PROSE)

Goal: make the outline *auditable* by adding an explicit planner stage that answers:

  • Do we have enough mapped evidence per H3?
  • Are the same few papers reused everywhere?
  • Are subsection axes still generic/scaffold-y?
  • Is the outline likely to produce a paper-like structure (final ToC budget: ~6–8 H2; fewer, thicker H3s)?

This is a deterministic “planner” unit: it must not write survey prose.

Inputs

Required:

  • outline/outline.yml
  • outline/mapping.tsv

Optional (best-effort diagnosis; may be missing early in the pipeline):

  • outline/OUTLINE_BUDGET_REPORT.md (if present: explains recent merges; helps interpret mapping/coverage changes)
  • papers/paper_notes.jsonl (for evidence levels)
  • outline/subsection_briefs.jsonl (for axis specificity)
  • GOAL.md (for scope drift hints)

Outputs

  • outline/coverage_report.md (bullets + small tables; NO PROSE)
  • outline/outline_state.jsonl (append-only JSONL; one record per run)

Workflow (planner pass, NO PROSE)

  1. Parse outline/outline.yml to enumerate H2 sections + H3 subsections (section sizing / budget).

- If outline/OUTLINE_BUDGET_REPORT.md exists, use it as the merge/change log so the coverage report can explain *why* structure changed.

  1. Read outline/mapping.tsv and compute per-H3 coverage and reuse hotspots.
  2. If papers/paper_notes.jsonl exists, summarize evidence levels (fulltext/abstract/title) for mapped papers.
  3. If outline/subsection_briefs.jsonl exists, compute axis specificity (generic vs specific axes) per H3.
  4. Optionally use GOAL.md to flag obvious scope drift (keywords not reflected in outline).
  5. Write outline/coverage_report.md and append a run record to outline/outline_state.jsonl.

Freeze policy

  • If outline/coverage_report.refined.ok exists, the script will not overwrite outline/coverage_report.md.

Script

Quick Start

  • python.codex/skills/outline-refiner/scripts/run.py --help
  • python.codex/skills/outline-refiner/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

  • Planner-pass diagnostics after section-mapper:

- python.codex/skills/outline-refiner/scripts/run.py --workspace workspaces/<ws>

Troubleshooting

Issue: report is missing evidence-level or axis-specificity columns

Cause:

  • Optional inputs are missing (no papers/paper_notes.jsonl and/or no outline/subsection_briefs.jsonl).

Fix:

  • Run paper-notes and/or subsection-briefs, then rerun outline-refiner.

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

31.1%
按下载量换算73

Gemini CLI

20.68%
按下载量换算49

Cursor

19.51%
按下载量换算46

Codex

13.56%
按下载量换算32

OpenCode

7.5%
按下载量换算18

Antigravity

3.84%
按下载量换算9

安全审计

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权限和风险

权限需确认

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安装前确认

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来源信息

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