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opener-variator开启变速装置

Agent Skill

opener-variator 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

559

周安装

24

GitHub Stars

422

下载量

196
CodexClaudeCursorGemini CLI

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

2

许可证

unknown

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:opener-variator(开启变速装置)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/opener-variator
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill opener-variator
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill opener-variator

简介

opener-variator 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装使用。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Opener Variator (H3 first paragraph rewrite)

Purpose: fix a high-signal automation tell that survives structural gates:

  • many H3s begin with the same rhetorical shape
  • "overview" narration replaces content-bearing framing

This skill is intentionally narrow:

  • only rewrite the first paragraph (or first 2–4 sentences) of the flagged H3 files
  • keep the argument moves and citations intact

Inputs

Required:

  • output/WRITER_SELFLOOP_TODO.md (Style Smells section)
  • the referenced sections/S<sub_id>.md files

Optional (helps you stay aligned):

  • outline/writer_context_packs.jsonl (use opener_mode, tension_statement, thesis)

Outputs

Note: keep this as an openers-last pass. Run it after paragraph-curator so you do not keep rewriting paragraph 1 while the body is still changing. Current compatibility policy: the script is marker-only; substantive opener repair should happen upstream in subsection-writer / chapter-lead-writer, not through blind regex rewriting.

  • Updated sections/S<sub_id>.md files (still body-only; no headings)

Workflow (route from the self-loop report)

  1. Open output/WRITER_SELFLOOP_TODO.md and locate ## Style Smells.
  2. Treat the flagged sections/S*.md list as the *only* scope for this pass.
  3. For each flagged file:

- Optional: look up its entry in outline/writer_context_packs.jsonl and read opener_mode / tension_statement / thesis to stay aligned. - Do the real rewrite upstream in subsection-writer or chapter-lead-writer; do not rely on blind local regex passes. - Best-of-3 opener sampling (recommended): draft 2-3 candidate opener paragraphs (different opener modes), then keep the one that is most content-bearing and least repetitive across H3s.

  1. Rerun writer-selfloop and confirm the Style Smells list shrinks.

Role prompt: Opener Editor (paper voice)

You are rewriting the opening paragraph of a survey subsection.

Goal:
- replace narration/overview openers with a content-bearing framing
- vary opener cadence across subsections so the paper reads authored

Constraints:
- do not invent facts
- do not add/remove/move citation keys
- do not change the subsection’s thesis

Checklist:
- sentence 1 is content-bearing (tension/decision/failure/protocol/contrast), not “what we do in this section”
- paragraph 1 ends with a clear thesis/takeaway
- no slide navigation (“Next, we…”, “In this subsection…”, “This section provides an overview…“)

What to delete (high-signal narration)

Rewrite immediately if the opener contains any of:

  • “This section/subsection provides an overview …”
  • “In this section/subsection, we …”
  • “This subsection surveys/argues …”
  • “Next, we move/turn …”
  • repeated opener labels (“Key takeaway:” spam)

What to replace with (opener moves)

Pick one opener mode per H3 (the writer pack may suggest opener_mode). Do not copy labels; write as natural prose.

Allowed opener moves (choose 1; keep it concrete):

  • Tension-first: state the real trade-off; why it matters; end with thesis.
  • Decision-first: frame the builder’s choice under constraints; end with thesis.
  • Failure-first: start from a failure mode that motivates the lens; end with thesis.
  • Protocol-first: start from comparability constraints (budget/tool access); end with thesis.
  • Contrast-first: open with an A-vs-B sentence, then explain why; end with thesis.
  • Lens-first: state the chapter lens and narrow to this subsection’s question.

Mini examples (paraphrase; do not copy)

Bad (overview narration):

  • This subsection provides an overview of tool interfaces for agents.

Better (content-bearing):

  • Tool interfaces define what actions are executable; interface contracts therefore determine which evaluation claims transfer across environments.

Bad (process narration):

  • In this subsection, we discuss memory mechanisms and then review retrieval methods.

Better (tension-first):

  • Memory improves long-horizon coherence, but it also expands the failure surface: retrieval can be stale, wrong, or adversarial, and agents rarely know which.

Done checklist

  • No flagged file starts with “overview/narration” stems.
  • Paragraph 1 ends with a thesis/takeaway (same meaning).
  • Citation keys are unchanged (no adds/removes/moves).
  • writer-selfloop still PASSes and Style Smells shrink.

Script

Quick Start

  • python.codex/skills/opener-variator/scripts/run.py --workspace workspaces/<ws>

All Options

  • --workspace <dir> (required)
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Rewrite openers in a survey workspace:

- python.codex/skills/opener-variator/scripts/run.py --workspace workspaces/survey-llm-agents

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

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

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

Codex

36.25%
按下载量换算71

Claude

29.89%
按下载量换算59

Cursor

17.35%
按下载量换算34

Gemini CLI

8.58%
按下载量换算17

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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