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Agent Skill · 研究检索

venture-delegation(风险投资代表团)

venture-delegation 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。

venture-delegation 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;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 33,416。

总安装 2,352周安装 134GitHub Stars 公开资料未说明下载 824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:venture-delegation(风险投资代表团)
来源仓库:https://github.com/dodge1218/venture-delegation
安装命令:
openclaw skills install venture-delegation
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 venture-delegation

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。

venture-delegation 将大型任务拆解为原子单元并分配至最优执行模块。适合在 OpenClaw 中管理复杂项目或自动化工作流分解场景。依赖 evals 机制识别子任务类型,自动选择最便宜且足够的执行者。使用前需定义清晰的任务边界与评估标准,避免过度拆分导致上下文丢失。

建议在小规模试点验证后再推广至全流程,降低调度失败风险。

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 venture-delegation;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

    它需要操作浏览器,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

venture-delegation 怎么安装?

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

venture-delegation 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。

venture-delegation 需要什么权限?

权限级别是操作浏览器。该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

venture-delegation 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。

venture-delegation 用的人多吗?

公开数据是累计安装 2,352 次,近一周安装 134 次,下载 824 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。

venture-delegation 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

  • 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

  • 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
venture-delegation

风险投资代表团 · 本页

2,352—操作浏览器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
delegation
version
1.0.0
description
Opus-level strategic decomposition for any opportunity, project, or task. Breaks work into atomic pieces with evals, assigns each to the cheapest capable model, and wires them into an executable pipeline. Use when starting anything new (opportunity, feature, project), when a task is too big to hand directly to a sub-agent, or when the user says "break this down", "figure out what to build", "analyze this opportunity". The THINK layer — Opus thinks, everything else works.

Delegation — Think Once, Execute Cheap

Opus is the brain. Everything else is hands. This skill is the translation layer between insight and execution.

Core Principle

Never use a $0.10/task model for $0.002/task work. Never use a $0.002/task model for $0.10/task thinking.

OPPORTUNITY ──► OPUS THINKS ──► ATOMIC TASK QUEUE ──► CHEAP MODELS EXECUTE ──► EVALS VERIFY ──► SHIP
                 (once)           (written artifact)     (sonnet/flash/gptoss)   (automated)

Phase 0: VENTURE EVAL (new ideas only — skip for defined tasks)

When Ryan has a raw idea, before any decomposition, run the Venture Eval Protocol. This replaces VC due diligence with builder-optimized evaluation. 3 rounds max, then decide.

Round 1: Irrational Optimism (Flash — cheap, fast)

Prompt a cheap model to go MAXIMUM bullish. No skepticism allowed.

You are an irrationally optimistic founder evaluating this idea: {IDEA}

Assume everything goes right. Answer:
1. WHAT: one sentence
2. WHO PAYS: specific buyer (not "businesses" — name the persona)
3. WHY NOW: what changed in the world that makes this possible today
4. TAM: bottoms-up, not "1% of $X billion" — how many buyers × price
5. UNFAIR EDGE: what do we already have (infra, data, distribution, skills)
6. FIRST $1K: exact steps to the first thousand dollars in revenue
7. 10X SCENARIO: what does this look like if everything works for 2 years
8. EXISTING ASSETS: which of our products/pipelines/skills does this plug into

Round 2: Brutal Fix (Sonnet — stronger reasoning)

Take Round 1's output and try to KILL it:

You are a ruthless VC partner reviewing this pitch: {ROUND_1_OUTPUT}

For each of the 8 points, either:
- CONFIRM: evidence supports it, cite why
- FIX: the claim is wrong but fixable — here's how
- KILL: this is fundamentally broken and unfixable — here's why

Then answer:
- BIGGEST RISK: the one thing that kills this
- CAN WE TEST IT FOR <$100?: yes/no + how
- COMPARABLE EXITS: 3 companies in adjacent space that sold/IPO'd
- VERDICT: BUILD / PARK / KILL (with one-sentence reason)

Round 3: Questionnaire (only if Round 2 says BUILD)

If the idea survives, generate 5 questions that MUST be answered before committing resources:

Based on this evaluated idea: {ROUND_2_OUTPUT}

Generate exactly 5 questions where:
- Each question can be answered with data (not opinion)
- Each answer changes the build plan materially
- Each can be researched in <30 minutes
- Format: QUESTION | HOW TO ANSWER | WHAT CHANGES IF YES vs NO

Ryan answers the 5 questions → answers feed into Phase 1 decomposition.

Token Budget for Venture Eval

RoundModelEst. TokensCost
R1: Optimismflash~1500$0.003
R2: Fixsonnet~2000$0.06
R3: Questionsflash~800$0.002
Total~4300$0.065

If we can't resolve it in $0.07 of reasoning, the idea isn't clear enough. Park it and revisit when more signal arrives.

When NOT to Venture Eval

  • Task is already defined (bug fix, feature request, maintenance)
  • Ryan explicitly says what to build
  • It's a client project with specs
  • It's infrastructure/tooling work

Phase 1: THINK (Opus only — ~500-2000 tokens output)

This is the ONLY phase that uses Opus. Everything after is delegated.

1a. Opportunity Frame (if Venture Eval was skipped)

Answer in ≤150 words:

WHAT: [one sentence — what is this]
WHO: [target customer — be specific, not "SMBs"]
WHY NOW: [timing signal — regulation, tech shift, market gap]
TAM: [total addressable market — even rough napkin math]
COMPETITORS: [top 3, their weakness]
OUR EDGE: [what we have that they don't — existing infra, distribution, data]
SLICE: [the specific wedge we'd enter with — not the whole market]

1b. Decompose into Atoms

Break the work into the smallest independently testable units.

Rules:

  • Each atom has ONE clear output (a file, a URL, a data point, a yes/no answer)
  • Each atom can be verified by a machine (not "looks good" — a command that returns pass/fail)
  • Each atom takes <15 min for a sub-agent
  • If an atom takes >15 min, it's not atomic — split again
  • Dependencies are explicit (atom B needs atom A's output file)

Output format:

| # | Atom | Output | Eval | Model | Depends | Est. |
|---|------|--------|------|-------|---------|------|
| 1 | Research competitor pricing | `research/pricing.md` | ≥3 competitors listed | flash | — | 3m |
| 2 | Scaffold Next.js app | `src/app/page.tsx` | `npm run build` exits 0 | sonnet | — | 5m |

1c. Model Assignment

Is it code generation?          → sonnet
Is it bulk/template/classify?   → flash
Is it batch of 20+?            → gptoss
Does it need >100K context?    → gemini-pro
Is it client-facing copy?      → opus (exception)
Is it a yes/no check?          → flash

Default: flash. Only upgrade when there's a reason.

1d. Eval Specification

Every atom gets a machine-verifiable eval:

Eval TypeExampleCheck
File existsresearch/pricing.mdtest -f research/pricing.md
Build passesNext.js buildsnpm run build; echo $? → 0
HTTP 200Site is livecurl -so /dev/null -w "%{http_code}" [url] → 200
Content check≥3 competitorsgrep -c "^##" research/pricing.md ≥ 3
ScreenshotUI renders correctlyBrowser screenshot + image model eval

Phase 2: PLAN (still Opus, just ordering — fast)

  1. Topological sort by dependencies
  2. Group into waves (parallel atoms)
  3. Estimate total time = longest path through dependency graph
  4. Estimate total cost = sum of (model cost × est. time)

Write full plan to workspace/DELEGATION_PLAN.md.

Phase 3: EXECUTE (Opus hands off — never touches work again)

Hand DELEGATION_PLAN.md to orchestrator → spawner pipeline.

Opus's ONLY role during execution: monitor completion events, re-route on failure.

Opus does NOT: write code, generate content, run builds, do research queries.

Phase 4: EVAL (automated)

After each atom: run eval command → pass/fail → retry with model escalation if needed.

fail → retry same model → upgrade model (flash→sonnet→opus) → mark failed

Phase 5: LEARN (feeds auto-improve)

Append timing + pass/fail per atom to .learnings/LEARNINGS.md.

Anti-patterns

  • ❌ Opus writing code
  • ❌ Subjective evals ("looks nice")
  • ❌ Atoms bigger than 15 min
  • ❌ Skipping the frame for new opportunities
  • ❌ Re-thinking during execution (plan is locked after Phase 2)
  • ❌ Spending >$0.07 reasoning about an unvalidated idea
  • ❌ Using gptoss for <20 items

平台分布

OpenClaw

89.91%
按下载量换算741

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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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