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auto-research-proposal汽车研究提案

Agent Skill

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。

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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

2

许可证

MIT-0

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: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。

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openclaw skills install auto-research-proposal

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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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。

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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

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