Launch Engine

Agentic pipeline that transforms ideas to revenue — for solo founders and bootstrappers.

Most MCP servers give you one tool. A GitHub integration. A database query. A Slack bot.

Asset Factory gives you42 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 withscoutand 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.

Add to yourclaude_desktop_config.json:

{ "mcpServers": { "asset-factory": { "command": "npx", "args": ["-y", "asset-factory-mcp"], "env": { "ASSET_FACTORY_PROJECT_DIR": "/path/to/your/project" } } } }

Add to your MCP settings (.cursor/mcp.json):

{ "mcpServers": { "asset-factory": { "command": "npx", "args": ["-y", "asset-factory-mcp"], "env": { "ASSET_FACTORY_PROJECT_DIR": "/path/to/your/project" } } } }

git clone https://github.com/ZionHopkins/asset-factory-mcp.git cd asset-factory-mcp npm install npm run build node dist/index.js

Asset Factory is atwo-layer tool system:

Layer A — 42 SOP Tools(read-only): Each tool validates prerequisites againstpipeline-state.json, loads upstream context from previous stages, checkslearnings.jsonfor 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.

Five entry points: 1. scout → Full pipeline (research → offer → build → deploy → validate) 2. rapid_test → Quick $50-100 test (signal in 3-5 days) 3. passive_deploy → Marketplace assets (after research) 4. tournament → Batch-evaluate 3-5 ideas through Layer 1 simultaneously 5. portfolio_triage → Rank existing pipelines by profit velocity, select top N

LAYER 1 (Strategist): scout → autonomy → market_intel → research → build_blocks → stress_test → unit_economics LAYER 2 (Builder): name_lock → platform + product → deploy → qa → validate_prep LAYER 3 (Validator): validate_check (daily) → validate_decide → feedback → iterate TRAFFIC (Paid): traffic_strategy → channels → creative_test → funnel_optimize → scale ORGANIC GROWTH (runs parallel with paid): content_engine → content_repurpose → seo_check (monthly) BOLD ACTION (post-QA): bold_action → credibility compression playbook REVENUE PHASE MANAGEMENT (optional overlay): portfolio_triage → revenue_review (weekly) Phases: Signal → Cash → Repeat → Scale CROSS-CUTTING: status | daily_check | lessons | voice_extract | dream_100 | tournament

Each tool checks prerequisites automatically. If you try to runresearchbefore completingmarket_intel, you'll get a clear STAGE_BLOCKED message telling you exactly what to run first.

Asset Factory creates and manages files in your project directory:

your-project/ ├── pipeline-state.json # Pipeline progress tracking ├── learnings.json # Pattern library across pipelines └── assets/ └── [market-name]/ ├── research/ # Scout reports, buyer research, market intel ├── building-blocks/ # The 7 Building Blocks ├── product/ # Product Architecture Blueprint ├── copy/ # Sales letters, email sequences ├── campaigns/ # Landing pages, ad copy ├── traffic/ # Traffic strategy, creative tests, analytics ├── validation/ # Deployment packages, daily checks, verdicts ├── voice/ # Brand voice calibration ├── passive-portfolio/ # PADA outputs ├── rapid-test/ # Rapid test assets ├── bold-action/ # Bold Action playbook └── content/ # Organic growth engine outputs ├── pillar/ # 2,000-4,000 word guides ├── spokes/ # 1,000-2,000 word pages ├── repurposed/ # Multi-platform assets per source ├── schema/ # JSON-LD files ├── seo-config/ # robots.txt, sitemap, brand signals └── audits/ # Monthly SEO/GEO audit reports

The project directory is resolved in order:
- ASSET_FACTORY_PROJECT_DIRenvironment variable
- --project-dir=CLI argument
- Current working directory

When you runstatuswith no existing pipeline, you'll see:
- 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)

- Start withstatus— always run this first. It reads your pipeline state and tells you exactly where you are and what to do next.
- New idea? Userapid_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.

- Follow the order— the prerequisite system exists for a reason. Each stage feeds the next. Skippingmarket_intelmeansresearchhas no competitive context. Skippingstress_testmeans you might build assets for a broken offer.
- Don't skipqa— it catches promise-product misalignment, unattributed statistics, and persona drift. Every asset that touches a buyer must clear the QA gate.
- Rundaily_checkevery dayduring validation — it takes 60 seconds and catches problems before they burn budget.
- Uselessonsafter every major decision— verdicts (ADVANCE/KILL), graduated rapid tests, creative test winners. The pattern library makes every future pipeline smarter.

- 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.

- 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 triggerpassive_compound. One proven asset can spawn 5-10 related assets.
- Runtraffic_analyticsweekly— attribution drift happens. What worked last week may not work next week. Stay on top of the data.

- Don't build assets beforestress_testpasses— a GO verdict means the offer is structurally sound. REVISE or REBUILD means fix the foundation first.
- Don't skipname_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 withoutqaclearance— unvetted copy with unattributed claims or persona misalignment damages trust and conversion rates.
- Don't run the full pipeline for every idea— that's whatrapid_testis for. Test 5-10 ideas cheaply, then invest the full pipeline in the winner.

Optional overlay that tracks revenue progression through four phases:

How to enable:Runportfolio_triageto select active pipelines. Selected pipelines getrevenue_phase: "signal". Runrevenue_reviewweekly to track progress.

scoutnow includes aSales Cycle Reality Checkthat estimates days-to-first-sale per market (GREEN <=14d, YELLOW 15-30d, RED >30d). This feeds into the Profit Velocity Score used byportfolio_triage.

The pipeline includes an automated QA test suite that runs at 3 points:

- test_landing_page.py— HTML structure, CTA presence, secret detection
- test_campaign_assets.py— Email/ad validation, brand consistency
- test_research_report.py— Section completeness, citation density, contradiction detection
- test_unit_economics.py— Margin positivity, CAC/LTV ratio, math verification

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