
Launch Engine — Business Execution OS
io.github.ZionHopkins由 GitHub 帳號 ZionHopkins 發布v1.1.0更新於 Nov 3, 2026
Agentic pipeline — 39 tools from idea to revenue for solo founders.
發布方自己的服務:Execution Market
概覽
本機 stdio MCP 伺服器,以 42 個 SOP 工具加 3 個檔案寫入工具,帶獨立創業者依階段走完從點子調研到付費流量驗證的創業流程。
- 可以做什麼
- 執行分階段商業流程:點子掃描、市場調研、買家人物誌、方案壓力測試與單位經濟試算
- 設計與產出素材:命名、平台與產品架構、銷售頁、電子郵件與廣告文案,並通過 QA 關卡
- 規劃與追蹤付費流量:渠道策略、創意測試、漏斗優化、放量與數據分析
- 經營自然成長:內容叢集、多平台改寫、SEO/GEO 稽核、品牌語氣萃取
- 管理組合:快速測試、多點子批次評估、被動式市場資產、營收階段檢視、每日檢查與經驗庫
- 寫入狀態與檔案:更新流程狀態、把素材存到專案目錄、記錄可重複使用的經驗
- 適用情境
- 適合希望讓 AI 助理依結構化、有前置條件的流程推進產品上市的獨立創業者,而非零散建議。前提是你願意照流程順序執行,並為狀態與產出準備一個專用專案目錄。
- 執行需求
- 以 stdio 在本機執行,通常透過 npx 使用 npm 套件。需要 Node.js,以及存放 pipeline-state.json 與 assets/ 的專案目錄,透過 LAUNCH_ENGINE_PROJECT_DIR 環境變數或專案目錄參數指定,預設使用目前工作目錄。README 另說明內建 QA 測試套件需要 Python 3.10+。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Launch Engine — Business Execution OS,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
Asset Factory
[npm version] [License: MIT] [GitHub stars]
Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.
Why Asset Factory?
Most MCP servers give you one tool. A GitHub integration. A database query. A Slack bot.
Asset Factory gives you 42 tools that work as a pipeline — the entire playbook from raw idea to validated revenue, running inside the AI client you already use.
- No more blank-page paralysis. Start with
scoutand the system tells you exactly what to do next, every step of the way. - Every stage feeds the next. Buyer research flows into offer design. Offer design flows into campaign copy. Campaign copy flows into validation. Nothing is wasted.
- Math before assets. Unit economics are validated before you build anything. You'll never spend weeks building an offer that can't work at your budget.
- Test ideas for $50, not $5,000.
rapid_testgives you signal in 3-5 days with a landing page and paid traffic — before you commit to the full pipeline. - Your AI becomes a co-founder, not a chatbot. It doesn't just answer questions. It executes a structured business system with you.
Install
Or run directly without installing:
Quick Start
Claude Desktop
Add to your claude_desktop_config.json:
Cursor
Add to your MCP settings (.cursor/mcp.json):
From Source
How It Works
Asset Factory is a two-layer tool system:
Layer A — 42 SOP Tools (read-only): Each tool validates prerequisites against pipeline-state.json, loads upstream context from previous stages, checks learnings.json for patterns, and returns full SOP instructions enriched with that context. Your AI executes the instructions.
Layer B — 3 Utility Tools (mutations): update_pipeline_state, save_asset, capture_learning. These handle all state writes and file creation. Your AI calls them after executing each SOP.
The Pipeline
Full Pipeline Flow
Each tool checks prerequisites automatically. If you try to run research before completing market_intel, you'll get a clear STAGE_BLOCKED message telling you exactly what to run first.
Tools Reference
SOP Tools (42)
Utility Tools (3)
Project Directory Structure
Asset Factory creates and manages files in your project directory:
Configuration
The project directory is resolved in order:
ASSET_FACTORY_PROJECT_DIRenvironment variable--project-dir=CLI argument- Current working directory
First Use
When you run status with no existing pipeline, you'll see:
Three paths available:
- rapid_test — $50-100 paid traffic test in 3-5 days
- scout — Full active pipeline with deep research and validation
- passive_deploy — Marketplace assets (requires research first)
Best Practices
Getting Started
- Start with
status— always run this first. It reads your pipeline state and tells you exactly where you are and what to do next. - New idea? Use
rapid_testfirst — don't run the full pipeline on an unvalidated idea. Spend $50-100 to get signal in 3-5 days. If it graduates, then runscout. - One pipeline at a time — you can run multiple rapid tests in parallel, but focus on one full pipeline at a time. Context switching kills momentum.
During the Pipeline
- Follow the order — the prerequisite system exists for a reason. Each stage feeds the next. Skipping
market_intelmeansresearchhas no competitive context. Skippingstress_testmeans you might build assets for a broken offer. - Don't skip
qa— it catches promise-product misalignment, unattributed statistics, and persona drift. Every asset that touches a buyer must clear the QA gate. - Run
daily_checkevery day during validation — it takes 60 seconds and catches problems before they burn budget. - Use
lessonsafter every major decision — verdicts (ADVANCE/KILL), graduated rapid tests, creative test winners. The pattern library makes every future pipeline smarter.
Working with the AI
- Let the AI execute the full SOP — each tool returns complete instructions. Don't interrupt midway. Let it finish the research, generate the deliverables, and save the files.
- Review Tier 3/4 decisions carefully — the system will pause and ask for your input on market selection, pricing, kill decisions, and anything involving real money. These pauses are intentional.
- Trust the math —
unit_economicswill tell you if the numbers work at your budget. If the verdict is NON-VIABLE, don't try to force it. Move on or adjust the offer.
Scaling
- Validate before you scale —
scalerequires proven creative winners with 30+ conversions. Scaling unvalidated campaigns is the fastest way to burn money. - Compound your learnings — passive assets that reach ANCHOR status should trigger
passive_compound. One proven asset can spawn 5-10 related assets. - Run
traffic_analyticsweekly — attribution drift happens. What worked last week may not work next week. Stay on top of the data.
Common Mistakes to Avoid
- Don't build assets before
stress_testpasses — a GO verdict means the offer is structurally sound. REVISE or REBUILD means fix the foundation first. - Don't skip
name_lock— changing the business name after assets are built means rebuilding everything. Lock it early. - Don't ignore KILL signals — if rapid test metrics hit kill thresholds, kill it. If validation says KILL, capture the lessons and move on. Sunk cost is not a strategy.
- Don't publish without
qaclearance — unvetted copy with unattributed claims or persona misalignment damages trust and conversion rates. - Don't run the full pipeline for every idea — that's what
rapid_testis for. Test 5-10 ideas cheaply, then invest the full pipeline in the winner.
Revenue Phase System (New in v1.2.0)
Optional overlay that tracks revenue progression through four phases:
How to enable: Run portfolio_triage to select active pipelines. Selected pipelines get revenue_phase: "signal". Run revenue_review weekly to track progress.
scout now includes a Sales Cycle Reality Check that estimates days-to-first-sale per market (GREEN <=14d, YELLOW 15-30d, RED >30d). This feeds into the Profit Velocity Score used by portfolio_triage.
Automated QA Test Suite (New in v1.1.0)
The pipeline includes an automated QA test suite that runs at 3 points:
Test modules in qa-tests/:
test_landing_page.py— HTML structure, CTA presence, secret detectiontest_campaign_assets.py— Email/ad validation, brand consistencytest_research_report.py— Section completeness, citation density, contradiction detectiontest_unit_economics.py— Margin positivity, CAC/LTV ratio, math verification
Requires: Python 3.10+
Listings
Listed on MCP Server Hub | MCP Registry
License
MIT
來源:README.md,提交 1d91652
工具
0版本歷史
1- v1.1.0最新Sep 16, 2026
評分
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