Measure and improve delays in realtime dashboards, data feeds, queues, and caches. Track response times, data age, and waiting work separately, then verify that changes improve the actual behavior of a deployed system.
Optimize and verify latency-sensitive systems — realtime dashboards, market data feeds, streaming agents, execution gateways, queues, and caches — by tracking p50/p95/p99 latency, freshness age, and queue depth, mapping hot paths, and running live readbacks. Use when p95 latency, throughput, or data freshness matters.
Review the instructions the agent will receive before you install.
Use this skill when the user cares about realtime behavior, hot paths, streaming freshness, or execution speed. This includes HFT-like infrastructure, but the skill is engineering-focused. It does not authorize live trading or financial advice.
Do not collapse everything into "fast." Track:
Write the path from user/event to final visible state:
source event -> provider API -> ingest worker -> queue -> cache -> edge route
-> client stream -> browser render -> user-visible state
Then measure each segment separately.
Use live readbacks when a deployed surface exists:
For market-data or execution-adjacent paths, also verify orderbook age, VWAP assumptions, provider status, and kill-switch behavior before calling the path ready.
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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.