Agent Workflows · By flonat

Create, revise, and evaluate reusable AI workflow skills, including trigger-quality tests.

flonat/flonat-research

Adversarially challenge research assumptions, mechanisms, and arguments in writing.

flonat/flonat-research

Save a compact continuation checkpoint so current work survives context compaction or a later session.

flonat/flonat-research

Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly.

flonat/flonat-research

Deliver a pre-flight duplicate check before creating new skills or agents.

flonat/flonat-research

Autonomously diagnose a codebase, apply minimal fixes, and rerun tests until they pass or a real blocker is reached.

flonat/flonat-research

Update current-focus.md with a structured session summary. An agent skill from flonat/flonat-research.

flonat/flonat-research

Update a project's own CLAUDE.md, README.md, or docs/ to reflect current state.

flonat/flonat-research

Add or repair an editable shared Python-package dependency across consuming projects, including clone-local configuration and verification.

flonat/flonat-research

Run an interactive one-question-at-a-time oral drill for research defence or active-recall study, escalating around weak answers and ending with a study sheet.

flonat/flonat-research

Create, accept, or update the persistent .context/ai-handoff.md state shared by AI sessions and machines.

flonat/flonat-research