Building a deterministic job-intel pipeline with OpenClaw assist
A developer has built findmejobs, a standalone Python pipeline for job hunting operations. The core design principle is to keep it boring and auditable.
Pipeline architecture
The pipeline handles scraping, normalization, and ranking within the application itself. OpenClaw is used only for two specific tasks: profile bootstrap and sanitized review/drafting. This creates strict trust boundaries between the deterministic pipeline components and the AI-assisted components.
Technical implementation
The system features deterministic ranking and rerunnable stages. It uses a SQLite database and follows a CLI-first workflow. The current scope is single-operator and single-host, running on a secondhand 2014 Mac Mini alongside OpenClaw.
Deliberate limitations
The developer intentionally excluded several features from the current scope:
- LinkedIn/Easy Apply scraping
- Auto-apply functionality
- Browser automation
- Fake "AI magic" ranking (though this might be considered in the future)
The developer is seeking to compare notes with others who have built OpenClaw-assisted workflows with strict trust boundaries.
📖 Read the full source: r/openclaw
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