Research Workflow
6 articles
Claude Code Skills Get Stale. Audit Them Quarterly.
Skills, memory entries, and hooks decay as models improve. In research, the decay can reach published findings and policy. A quarterly audit protocol.
Building a Literature Surveillance System
Free tools like Google Scholar alerts and Semantic Scholar already monitor academic literature. What an AI coding assistant adds is the glue: combining sources, following citation networks, and catching the quiet failures that make AI-gathered references dangerous.
The Data We Forgot We Had: A Tagging System for Research Serendipity
Tag datasets by the questions they can answer, not just what they contain. A question-first system makes dormant research data discoverable when new questions arise.
47 Scripts to 15: Cleaning a Research Codebase
Research projects accumulate code debt. An AI agent can map dependencies, identify dead code, and reorganize months of accumulated scripts, with counterfactual tests to verify nothing broke.
From Methods Paragraph to Working Pipeline: AI-Assisted Implementation
A well-written methodology section is almost executable code. The gap between describing a procedure and implementing it has narrowed dramatically with agent-based coding tools.
One Context File, Zero Re-Explanations: Teaching AI Your Research Project
Every new coding session with AI starts the same way: re-explaining the project. A single CLAUDE.md file loads automatically every session. Write the context once; the agent reads it every time.