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subsection-briefs小节简介

Agent Skill

subsection-briefs 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

768

周安装

33

GitHub Stars

422

下载量

269
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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:subsection-briefs(小节简介)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/subsection-briefs
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill subsection-briefs
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 subsection-briefs

简介

用于查找和检索相关信息,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。
  • 当前顶部介绍:subsection-briefs 用于查找、检索和筛选相关信息。
  • 当前底部简介为空,暂无补充说明。

SKILL.md

Subsection Briefs

Build deterministic H3 brief cards from outline + mapping + paper notes.

Compatibility mode is active: this skill keeps the current outline/subsection_briefs.jsonl field contract and paragraph-plan shape while moving phrase/domain logic into references/ and assets/.

Quick Use

  • Run scripts/run.py as the deterministic materializer.
  • Keep the output NO PROSE: subsection-scoped plans, axes, clusters, and bridge handles only.
  • Preserve current downstream compatibility for transition-weaver, writer-context-pack, and subsection-writer.

Load Order

Always read:

  • references/overview.md

Read by task:

  • If thesis feels repetitive or copyable, read references/thesis_patterns.md.
  • If tension_statement is too generic, read references/tension_patterns.md.
  • If axes are weak or domain-biased, read references/axis_catalog_generic.md and references/axis_catalog_llm_agents.md.
  • If transition handles feel bland, read references/bridge_terms.md.
  • For calibration, read references/examples_good.md.

Machine-readable assets:

  • assets/phrase_packs/thesis_patterns.json
  • assets/phrase_packs/bridge_contrast.json
  • assets/domain_packs/generic.json
  • assets/domain_packs/llm_agents.json
  • assets/domain_packs/embodied_ai.json
  • assets/domain_packs/text_to_image.json

The script loads these packs first; patch them before changing Python when the issue is phrasing, domain routing, axis inventory, cluster purity, or lexical bridge coverage.

Inputs

  • outline/outline.yml
  • outline/mapping.tsv
  • papers/paper_notes.jsonl
  • Optional: GOAL.md
  • Optional: outline/claim_evidence_matrix.md

Output

  • outline/subsection_briefs.jsonl

Required record shape remains compatibility-preserving:

  • identity: sub_id, title, section_id, section_title
  • planning core: rq, thesis, scope_rule, axes, bridge_terms, contrast_hook, tension_statement
  • evidence hooks: evaluation_anchor_minimal, required_evidence_fields, clusters
  • execution plan: paragraph_plan, evidence_level_summary, generated_at

What run.py Should Do

  • Read outline, mapping, and notes.
  • Normalize subsection seeds from outline bullets.
  • Load thesis/tension/domain-axis packs from assets/.
  • Produce stable JSONL records with the existing contract.

What run.py Should Not Do

  • Do not invent papers, citations, or claims.
  • Do not emit reader-facing narrative prose.
  • Do not hardcode domain-specific sentence templates when an asset pack can hold them.

Block / Reroute

  • If outline, mapping, or notes are missing, stop.
  • If evidence is thin, keep thesis/tension_statement conservative and let downstream evidence skills strengthen the subsection.
  • If contrast clusters collapse into overlapping paper pools, reroute before writing: after removing bridge papers, each side should still retain at least 2 unique papers.
  • Use bridge_terms to surface concrete lexical handles that later evidence/ranking stages can still match (OOD, sim-to-real, world model, failure detector, specific benchmark families), not only generic axis names.
  • Prefer domain-pack cluster_rules over ad-hoc bootstrap overlaps when the mapped set is already large enough to support disjoint clusters.
  • Do not “fix” thin evidence by inventing more specific axes or stronger claims.

Execution notes

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

  • outline/outline.yml for section/subsection structure
  • outline/mapping.tsv for paper-to-subsection coverage
  • papers/paper_notes.jsonl for structured evidence
  • GOAL.md for topic/domain cues
  • outline/claim_evidence_matrix.md as optional supporting context when present

Script

Quick Start

  • python.codex/skills/subsection-briefs/scripts/run.py --workspace <workspace_dir>

All Options

  • --workspace <dir>
  • --unit-id <id>
  • --inputs <a;b;...>
  • --outputs <a;b;...>
  • --checkpoint <C*>

Examples

  • python.codex/skills/subsection-briefs/scripts/run.py --workspace workspaces/<ws>

Troubleshooting

  • If the wrong domain pack is selected, inspect GOAL.md and the asset packs before changing the script.
  • If briefs sound too generic, adjust the phrase/domain packs instead of adding more Python prose.
  • If papers/paper_notes.jsonl is thin, reroute to note extraction rather than inventing axes.

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

27.06%
按下载量换算73

Gemini CLI

24.52%
按下载量换算66

Cursor

18.21%
按下载量换算49

Codex

12.83%
按下载量换算35

OpenCode

7.87%
按下载量换算21

Antigravity

2.99%
按下载量换算8

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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