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Agent Skill · 研究检索

auto-research-proposal(汽车研究提案)

auto-research-proposal 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

auto-research-proposal 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 31,516。

总安装 2,688周安装 112GitHub Stars 公开资料未说明下载 896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-0
版本
1.0.0

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:auto-research-proposal(汽车研究提案)
来源仓库:https://github.com/frankaging/auto-research-proposal
安装命令:
openclaw skills install auto-research-proposal
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install auto-research-proposal

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。

auto-research-proposal 是多智能体研究作战室,支持角色辩论和提案撰写。适合在 OpenClaw 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。具备人物角色持久性和漂移检测功能,提升研究提案质量。

安装命令:openclaw skills install auto-research-proposal;需确认权限范围和维护状态。注意是否会触发联网、命令执行或文件读写操作,建议结合原始 README 核验具体用法。

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 OpenClaw;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 openclaw skills install auto-research-proposal;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

auto-research-proposal 怎么安装?

在终端执行 openclaw skills install auto-research-proposal。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

auto-research-proposal 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

auto-research-proposal 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install auto-research-proposal 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

auto-research-proposal 是免费的吗?

来源标注的许可证是 MIT-0。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。

auto-research-proposal 用的人多吗?

公开数据是累计安装 2,688 次,近一周安装 112 次,下载 896 次。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。

auto-research-proposal 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 OpenClaw,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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
auto-research-proposal

汽车研究提案 · 本页

2,688—执行命令MIT-0有提醒

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

name
war-room
type
agentic
description
>
depends_on

War Room Skill

A war room runs personas through a structured two-phase session:

  1. Phase 1 — Ideation (5 rounds max): personas debate the research idea question by question.

Ends early on consensus (all [AGREE] or [PASS], no [OBJECT]) or two consecutive all-[PASS] rounds. Output: memory/war-room/idea-snapshot.md

  1. Phase 2 — Proposal (5 rounds max): personas collaboratively draft each proposal section.

Output: memory/war-room/proposal-draft.md


Configurations

Defined in skills/war-room/personas/agents.json under the configurations key.

NameParticipantsUse when
fullCreative, Senior Prof, Young Faculty, Industry LiaisonDeep ideation, novel directions, full proposal needed
1on1Senior Professor, Young FacultyQuick feasibility check, tight scope

The configuration is specified in PROJECT.md frontmatter under war_room.configuration. Default: full.


Consensus Protocol

Every persona response must end with one of:

TagMeaning
[AGREE]I accept the current position
[PASS]No strong view either way
[OBJECT: reason]I reject — give specific reason

Do not embed tags mid-response. They must be the final line.


Single-Agent Fallback (Codex / no subagent spawning)

If you cannot spawn subagents, run the same loop inline. For each persona turn:

  1. Print a clear header: --- [Round N] <Persona Name> ---
  2. Adopt that persona fully for your response — voice, stance, debate style, red lines.
  3. End with the consensus tag ([AGREE], [PASS], or [OBJECT: reason]).
  4. Return to moderator role between turns to write the log entry and check consensus.

Drift detection still applies: before each turn, re-read the persona definition and note whether your previous response for that persona drifted from their stance.


Playbook

Follow this sequence exactly. You are the moderator/orchestrator throughout.

Setup

  1. Spawn the monitor in a new terminal window so the user can watch the session live:

- macOS:

     osascript -e 'tell application "Terminal" to do script "cd '"'"'<repo_root>'"'"' && python skills/war-room/monitor.py --project <project_path>"'

- Linux (with display):

     xterm -title "War Room Monitor" -e "python skills/war-room/monitor.py --project <project_path>" &

- If the spawn fails, continue silently — the monitor is optional.

Replace <repo_root> with the absolute path to this repo and <project_path> with the active project folder path. The monitor requires rich (pip install rich).

  1. Read skills/war-room/personas/agents.json. Load the selected configuration's participant list and synthesiser index.
  2. For each participant, read their persona file (e.g. skills/war-room/personas/senior-professor.md).
  3. Read skills/persistent-persona/SKILL.md and skills/memory-checkpoint/SKILL.md — you will apply these every turn.
  4. Check if memory/war-room/discussion-log.md exists.

- If yes: load it — you are resuming a session. Read memory/SUMMARY.md for current phase and round. - If no: create memory/war-room/discussion-log.md with a header block (project name, configuration, timestamp).

  1. Ensure memory/.private/ folder exists for persona memos.

Phase 1 — Ideation

For each round (up to 5):

For each participant in configuration order:

a. Prepare persona context - Read memory/.private/agent-<index>-memo.md if it exists. - Check the most recent Drift flag. If yes, prepend a PERSONA RESET block to the subagent SI:

    PERSONA RESET: I am <persona name>. My core stance: <one-line from persona file>.
    I may have drifted last round. I am recommitting before engaging.
    I will not update positions without new evidence.

b. Call Agent subagent with this SI (in order): - first-principles content - Persona definition (full persona file content) - persistent-persona skill instructions - (if drift) PERSONA RESET block - The discussion log so far

User message: "It's your turn. Respond to the discussion. End your response with [AGREE], [PASS], or [OBJECT: reason]."

c. Append to log - Append the response to memory/war-room/discussion-log.md in this format:

    ---
    **[Round N] <Persona Name>**
    <response text>

d. Write persona memo - Append to memory/.private/agent-<index>-memo.md:

    ## <YYYYMMDD_HHMMSS>
    **Persona**: <name>
    **Session summary**: <one sentence>
    **Position changes**: <what changed, what evidence caused it>
    **Pressure events**: <did the persona hold under pressure?>
    **Mental state**: <reasoning from own stance or mirroring group?>
    **Drift flag**: <yes | no>

After all participants have spoken in a round:

e. Check consensus - If all responses end with [AGREE] or [PASS] and no [OBJECT]: Phase 1 ends early. - If two consecutive rounds were all [PASS]: Phase 1 ends early.

f. Checkpoint - Write memory/checkpoints/<timestamp>/agent-0.md (your moderator state: current round, phase, next action). - Update memory/SUMMARY.md.

g. Synthesiser snapshot (after round 3 or on early exit) - Call a subagent with the synthesiser persona + discussion log. - Task: "Summarise the agreed research idea so far in 3-5 bullet points." - Write output to memory/war-room/idea-snapshot.md.

Phase 2 — Proposal

Read skills/research-proposal/SKILL.md now. The proposal has 6 sections.

Assign one section per round (or pair related sections). Run the same per-turn loop as Phase 1, but each subagent's task is:

"Draft your contribution to Section N: <section name>. Build on what others have written. End with [AGREE], [PASS], or [OBJECT: reason]."

After each round, the synthesiser appends the agreed section draft to memory/war-room/proposal-draft.md.

Finalization

  1. Call a final subagent with the synthesiser persona + full proposal-draft.md.

Task: "Produce the final clean research proposal. Follow the research-proposal skill format exactly. 2 pages max."

  1. Write output to memory/war-room/proposal-draft.md (overwrite with final version).
  2. Copy discussion-log.md, idea-snapshot.md, and proposal-draft.md to <sandbox_root>/results/.
  3. Write final checkpoint and update memory/SUMMARY.md with status: complete.

Outputs

<project>/
├── memory/
│   ├── SUMMARY.md                        ← current phase, round, resume point
│   ├── war-room/
│   │   ├── discussion-log.md             ← full turn-by-turn transcript (live)
│   │   ├── idea-snapshot.md              ← synthesised agreed idea (written after round 3+)
│   │   └── proposal-draft.md            ← accumulated proposal sections → final output
│   ├── .private/
│   │   ├── agent-1-memo.md              ← persona drift memo (private, append-only)
│   │   ├── agent-2-memo.md
│   │   ├── agent-3-memo.md
│   │   └── agent-4-memo.md
│   └── checkpoints/
│       └── <timestamp>/
│           └── agent-0.md               ← orchestrator state (phase, round, next action)
└── results/                             ← copied here at session end
    ├── discussion-log.md
    ├── idea-snapshot.md
    └── proposal-draft.md

The monitor (skills/war-room/monitor.py) reads memory/war-room/discussion-log.md, memory/SUMMARY.md, memory/war-room/idea-snapshot.md, and memory/.private/ in real time.

平台分布

OpenClaw

71.63%
按下载量换算642

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install auto-research-proposal 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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