Daaf

Summary: What is DAAF?

LLM-based AI assistants are becoming increasingly capable, but they are always at risk of hallucination, sycophancy, over-confidence, and laziness. So can these flawed and non-deterministic tools ever be useful for conducting rigorous data analysis?

Yes -- but only with the right guidance, right guardrails, and in expert hands to direct all core decisions and verify all key outputs.

Enter DAAF, the Data Analyst Augmentation Framework. DAAF is a free and open-source instructions framework for Claude Code that helps skilled researchers deepen and extend their expertise across any domain of data analysis with AI assistance -- while enhancing the transparency, rigor, and reproducibility that good science demands. DAAF sits between you and Claude Code to automatically and consistently help Claude think more like a responsible and rigorous researcher by:

- Enforcing strict auditability and reproducibility standards for all work, thus allowing you to verify everything Claude does on your behalf
- Preventing potentially dangerous unintended file access and editing, by sandboxing Claude with strict protections and logging traces
- Setting high standards of care, rigor, and thoroughness in all data analysis, by forcing Claude to comment, verify, and review all analytic code before you ever see it
- Embedding best practices for a wide variety of research methodologies like causal inference and geospatial analysis, by providing rich Skills (reference manuals Claude loads on demand when a task calls for them) that extend Claude's base capabilities with real research and resources
- Collaborating with you, the human expert, directly on all key decisions, thus keeping you firmly in the driver's seat

Think of it as a force-multiplying exoskeleton for human…