Change an existing feature that works but needs different behavior. The skill updates tests to reflect the new requirements, adjusts the implementation, and coordinates review with approval checkpoints before committing the changes.
Orchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.
Review the instructions the agent will receive before you install.
Actor · action · target: orch · change · feature. Thin wrapper over the
shared engine in orch-pipeline.
orch-fix-defect (no bug to reproduce).orch-add-feature (the capability already exists).orch-pipeline engine with the settings above.standard+ size warrants the full planner pass.security-reviewer if the change touches a security trigger.orch-change-feature: make nws-poller alert at 2 warnings instead of 3
→ update threshold tests to new spec → change impl to green
→ code-review → commit [GATE 2: confirm]
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
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 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.
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.