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venture-delegation风险投资代表团

Agent Skill

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。

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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: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。

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openclaw skills install venture-delegation

简介

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。

SKILL.md

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

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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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