Coordinate code refactoring, restructuring code while preserving its behavior. Check existing tests before changes, work in small steps, review the result, and pause for approval of the plan and before committing.
Orchestrate a behavior-preserving refactor — confirm tests are green, restructure without changing behavior, keep tests green, review, and gated commit. Use when the structure should improve but behavior must not change.
Review the instructions the agent will receive before you install.
Actor · action · target: orch · refine · code. Thin wrapper over the shared
engine in orch-pipeline.
orch-change-feature / orch-fix-defect).orch-pipeline engine with the settings above.refactor-cleaner agent
(it runs knip / depcheck / ts-prune and removes safely).refactor: — the diff must be behavior-neutral.orch-refine-code: extract the NWS HTTP client out of poller.py
→ confirm tests green → plan extraction [GATE 1: approve]
→ move in small steps, tests green throughout → code-review
→ commit refactor: [GATE 2: confirm]
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Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.
Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.
Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.
Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.
Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.