Turn notes, transcripts, or research into polished articles, guides, tutorials, and newsletters. Use supplied writing examples or brand guidance to maintain a consistent voice, organize a clear argument, and support factual claims with provided evidence.
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
Review the instructions the agent will receive before you install.
Write long-form content that sounds like an actual person with a point of view, not an LLM smoothing itself into paste.
If the user wants a specific voice, run brand-voice first and reuse its VOICE PROFILE.
Do not duplicate a second style-analysis pass here unless the user explicitly asks for one.
If no voice references are given, default to a sharp operator voice: concrete, unsentimental, useful.
Delete and rewrite any of these:
Before delivering:
VOICE PROFILENo 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 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.