Autonomous content pipeline that captures what I'm working on, generates posts across platforms, and learns from what actually performs.
I built this because I was spending 20 minutes per LinkedIn post doing the same thing every time — pulling context from session notes, structuring it against my voice, posting, then never looking at what worked. That's a system, not a creative act. So I automated the system and kept the creative part.
- Captures daily context from my Claude Code session history and project notes — what I shipped, what I learned, what failed
- Generates content using semantic blueprints that encode my voice, brand constraints, and platform rules
- Posts to LinkedIn via OAuth with draft review, scheduling, and approval gates — nothing goes out without my sign-off
- Tracks engagement and feeds performance data back into the pipeline so the system learns what lands
Current status: Phase 3 complete (26/26 stories). Semantic blueprints, multi-agent validation, and content generation all working. Feedback loops (Phase 6) are next.
Context Capture → Blueprint Engine → Multi-Agent Pipeline → Platform Posting
(sessions, notes) (STF, MRS, SLA, PIF) (generate → validate → refine) (LinkedIn, drafts, scheduling)
↑ │
└──── engagement feedback ─────┘
The pipeline uses a Generator → Validator → Refiner pattern. Llama drafts, Claude validates against brand constraints, refinement happens in-loop. Blueprints aren't prompts — they're structured decision frameworks that encode what makes a post mine vs. generic AI slop.
- Python 3.11+ with uv for package management
- AI: Ollama locally (llama3:8b, free) → AWS Bedrock in production (Claude Haiku + Llama 3.3 70B, ~$0.004/post)
- Database: SQLite dev → PostgreSQL production, Alembic migrations
- Testing: 403 tests passing, ruff compliant, mypy typed
- Deployment: Self-hosted,
./scripts/deploy.sh
git clone <repo-url> && cd ContentEngine
uv sync
cp .env.example .env # add LinkedIn credentials
uv run alembic upgrade head
uv run content-engine capture-context
uv run content-engine generate --pillar what_building --framework STF# Context capture
uv run content-engine capture-context # today's context from sessions + notes
uv run content-engine capture-context --date 2026-01-12
# Content generation
uv run content-engine blueprints list # available frameworks
uv run content-engine generate --pillar what_building --framework STF
uv run content-engine sunday-power-hour # batch workflow for the week
# Post management
uv run content-engine draft "Your content here"
uv run content-engine approve 1 # review then post
uv run content-engine approve 1 --dry-run # test without posting
# Analytics
uv run content-engine collect-analytics
python scripts/analytics_dashboard.py- Phase 1: LinkedIn OAuth + posting infrastructure
- Phase 1.5: Database, CLI, scheduled posting
- Phase 2: Context capture from session history
- Phase 3: Semantic blueprints (26/26 stories)
- Phase 4: Brand Planner agent
- Phase 5: Autonomous content generation
- Phase 6: Engagement feedback loops
- Phase 7: Multi-platform (Twitter, blog, YouTube)
Running the full pipeline on AWS Bedrock: < $1.50/year for 30 posts/month. Local dev with Ollama is free.
MIT