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.
- Adding descriptive labels to icon-only buttons
- Reviewing keyboard navigation and modal focus
- Checking contrast and control sizes
affaan-m/ECC☆ 283.2KUpdated Oct 16, 2026
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.
- Investigating failures that appear only inside an agent application
- Tracing old conversation content leaking into new sessions
- Checking skipped or falsely claimed tool execution
affaan-m/ECC☆ 283.2KUpdated Oct 16, 2026
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.
- Comparing coding agents on real development tasks
- Evaluating a new tool or model before adoption
- Checking performance after agent updates
affaan-m/ECC☆ 283.2KUpdated Oct 16, 2026
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.
- Define an AI agent’s tool set and input formats
- Make tool results and next steps easier to interpret
- Specify safe retries and stopping conditions
affaan-m/ECC☆ 283.2KUpdated Oct 16, 2026
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.
- Diagnosing repeated tool calls with no progress
- Investigating quality loss as context grows
- Checking file paths and environment mismatches
affaan-m/ECC☆ 283.2KUpdated Oct 16, 2026
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.
- Diagnosing repeated failures in an agent run
- Investigating loops through the same tool calls
- Reviewing output degradation as context grows
affaan-m/ECC☆ 283.2KUpdated Oct 5, 2026