cm-autopilot
Easy-to-use conversational CLI (Claude Code style) for non-technical users to spawn parallel AI tasks supervised by a visual web dashboard.
Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.
$ npx -y skills add tody-agent/codymaster --skill cm-content-factory --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cm-content-factoryContext preview
The summary Claude sees to decide when to auto-load this skill.
Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.
name: cm-content-factory description: "Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing."
Config-driven, **self-improving** content factory with **real-time dashboard**, **multi-agent independence**, and **token management**. Gets smarter with use through memory + reward system.
┌─────────────────────────────────────────────┐
│ 🌐 DASHBOARD (localhost:5050) │
│ Pipeline │ Tasks │ Tokens │ Logs │ Landing │
└───────────┬─────────────────────────────────┘
│ SSE / Polling
┌───────────┴─────────────────────────────────┐
│ 🏭 PIPELINE ENGINE │
│ ┌──────────┐ ┌──────────┐ ┌────────────┐ │
│ │ State │ │ Token │ │ Agent │ │
│ │ Manager │ │ Manager │ │ Dispatcher │ │
│ └──────────┘ └──────────┘ └────────────┘ │
│ │ │
│ EXTRACT → PLAN → WRITE → AUDIT → SEO → PUB│
│ │ │ │
│ 📊 SCOREBOARD (reward/penalty) │
│ │ │
│ 🧠 MEMORY (3-layer learning) │
└─────────────────────────────────────────────┘**Config file**: `content-factory.config.json` at project root. Schema: `config.schema.json`.
---
# New project (interactive wizard) python3 scripts/wizard.py # Full pipeline WITH dashboard python3 scripts/pipeline.py --dashboard # Pipeline with budget limit python3 scripts/pipeline.py --dashboard --budget 5.0 # Dashboard only (standalone) python3 scripts/dashboard_server.py
AI MUST ask 5 question groups in order:
| # | Group | Key Questions | |---|-------|---------------| | Q1 | Niche Info | Industry, brand, address, phone, USP | | Q2 | Reference & Avoid | Reference websites, styles to avoid, tone | | Q3 | Data Sources | Existing files, URLs to extract, images | | Q4 | Content Goals | Number of articles, keywords, language, region | | Q5 | Deploy | Cloudflare account, domain, milestone |
Display summary table → **WAIT for user OK** → then proceed.
---
| Mode | Script | Purpose | |------|--------|---------| | 📦 EXTRACT | `extract.py` | Source docs → JSON knowledge-base | | 📋 PLAN | `plan.py` | Knowledge → topic queue | | ✍️ WRITE | `write.py` | AI content generation (batch/single) | | 🔍 AUDIT | `audit.py` | Quality check + auto-fix | | 🔎 SEO | `seo.py` | Metadata optimization | | 🚀 PUBLISH | `publish.py` | Build + deploy | | 🧠 LEARN | `scoreboard.py` + `memory.py` | Extract patterns from feedback | | 🔬 RESEARCH | `research.py` | Auto-research new topics | | 💰 REVIEW | `monetize.py` | Monetization scoring | | 🏭 PIPELINE | `pipeline.py` | Full automated A→Z | | 📊 DASHBOARD | `dashboard_server.py` | Real-time web dashboard | | 🎯 LANDING | `landing_generator.py` | Persona-based landing pages |
All scripts: `python3 scripts/<script> --config content-factory.config.json`
---
Real-time web dashboard at `http://localhost:5050`:
# Auto-start with pipeline python3 scripts/pipeline.py --dashboard --dashboard-port 5050 # Standalone python3 scripts/dashboard_server.py --port 5050
---
Multiple agents can work independently on the same pipeline via file-based task queue.
from agent_dispatcher import AgentDispatcher
d = AgentDispatcher()
# Enqueue tasks
d.enqueue("write-article-1", "write", {"topic": "SEO Tips"}, priority=3)
d.enqueue_batch([{"id": "w-2", "type": "write"}, {"id": "w-3", "type": "write"}])
# Agent claims next task
task = d.claim_next("gemini-agent-1")
d.heartbeat("gemini-agent-1", task["id"]) # Keep alive
# Complete or fail
d.complete(task["id"], "gemini-agent-1", {"result": "ok"})
d.fail(task["id"], "gemini-agent-1", "API timeout") # Auto-retry up to 3xFeatures: priority ordering, stale lock detection (10min), auto-retry (3x), heartbeat.
---
Track token usage, costs, rate limits, and budget across all providers.
from token_manager import TokenManager
tm = TokenManager(budget_usd=5.0)
# Record usage
tm.record_usage("gemini", input_tokens=1000, output_tokens=500, task_id="w-1")
# Check budget
if not tm.check_budget():
print("Budget exceeded!")
# Rate limiting
tm.wait_if_rate_limited("gemini")
# Circuit breaker (auto-stop after 5 consecutive failures)
if tm.is_circuit_open("gemini"):
print("Provider down, switching...")python3 scripts/token_manager.py status
---
Generate persona-based landing pages using Content Mastery SB7 framework.
Add `personas` array to config:
{
"personas": [{
"name": "Economic Buyer",
"headline": "Save 50% on Marketing Costs",
"subheadline": "AI creates professional content, 10x faster",
"pain_points": ["High marketing costs", "Lack of content staff"],
"benefits": [{"title": "Cost Savings", "description": "Reduce costs by 50%"}],
"social_proof": [{"number": "2,347", "label": "Businesses trust us"}],
"steps": [{"title": "Configure", "description": "Enter your business information"}],
"cta_text": "Try It Free"
}]
}python3 scripts/landing_generator.py --config content-factory.config.json python3 scripts/landing_generator.py --config content-factory.config.json --list
---
1. IN
"I can't write code. But in 6 months, I shipped 12 real products using AI. CodyMaster is everything I learned — so you don't have to repeat my mistakes." — Tody Le, Head of Product, Creator of CodyMaster 50+ skills. One install.
Repo: tody-agent/codymaster
Easy-to-use conversational CLI (Claude Code style) for non-technical users to spawn parallel AI tasks supervised by a visual web dashboard.
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