/latency-critical-systems
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
Claude Code
$ npx -y skills add affaan-m/ECC --skill latency-critical-systems --agent claude-code
Codex
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a codex -y
Cursor
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a cursor -y
Gemini CLI
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a gemini-cli -y
GitHub Copilot
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a github-copilot -y
OpenCode
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a opencode -y
Windsurf
$ npx skills add affaan-m/ECC --skill latency-critical-systems -a windsurf -y
SKILL.md
latency-critical-systems.SKILL.mdname: latency-critical-systems
description: 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.
license: MIT
metadata:
origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob
Latency Critical Systems
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.
Split The Metrics
Do not collapse everything into "fast." Track:
- p50, p95, and p99 latency;
- throughput;
- freshness age;
- queue depth;
- cache hit rate;
- provider/API response time;
- browser render time;
- correctness under load;
- failure and retry behavior.
Map The Hot Path
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.
Optimization Order
1. Remove unnecessary round trips. 2. Cache stable reads with freshness metadata. 3. Batch small calls and writes. 4. Move compute closer to the data or the user. 5. Split hot and cold paths. 6. Apply backpressure before queues grow unbounded. 7. Use streaming only when it improves freshness or user experience. 8. Add canaries for stale data, degraded providers, and bad cache state.
Verification
Use live readbacks when a deployed surface exists:
- HTTP timing and response headers;
- provider freshness timestamp;
- queue or job state;
- edge/cache state;
- browser verification for actual UI freshness;
- logs around retries and degraded mode.
For market-data or execution-adjacent paths, also verify orderbook age, VWAP assumptions, provider status, and kill-switch behavior before calling the path ready.
Guardrails
- Do not optimize latency by dropping required validation.
- Do not hide stale data behind fast cache hits.
- Do not claim millisecond behavior from client labels without measurement.
- Do not run live orders, destructive migrations, or customer-impacting deploys
without an explicit approval gate.
- Keep secrets and private payloads out of logs and benchmark artifacts.
Read more
name: latency-critical-systems description: 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. license: MIT metadata: origin: ECC tools: Read, Write, Edit, Bash, Grep, Glob
Latency Critical Systems
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.
Split The Metrics
Do not collapse everything into "fast." Track:
- p50, p95, and p99 latency;
- throughput;
- freshness age;
- queue depth;
- cache hit rate;
- provider/API response time;
- browser render time;
- correctness under load;
- failure and retry behavior.
Map The Hot Path
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.
Optimization Order
1. Remove unnecessary round trips. 2. Cache stable reads with freshness metadata. 3. Batch small calls and writes. 4. Move compute closer to the data or the user. 5. Split hot and cold paths. 6. Apply backpressure before queues grow unbounded. 7. Use streaming only when it improves freshness or user experience. 8. Add canaries for stale data, degraded providers, and bad cache state.
Verification
Use live readbacks when a deployed surface exists:
- HTTP timing and response headers;
- provider freshness timestamp;
- queue or job state;
- edge/cache state;
- browser verification for actual UI freshness;
- logs around retries and degraded mode.
For market-data or execution-adjacent paths, also verify orderbook age, VWAP assumptions, provider status, and kill-switch behavior before calling the path ready.
Guardrails
- Do not optimize latency by dropping required validation.
- Do not hide stale data behind fast cache hits.
- Do not claim millisecond behavior from client labels without measurement.
- Do not run live orders, destructive migrations, or customer-impacting deploys
without an explicit approval gate.
- Keep secrets and private payloads out of logs and benchmark artifacts.
Your agent can write code, but ECC gives it a coordinated engineering system and toolbox: it plans before it builds, verifies changes with tests, reviews its own work from a fresh context, remembers what matters, and turns repeated wins into reusable skills
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Questions about latency-critical-systems.
- Which coding agents can use latency-critical-systems?
- latency-critical-systems installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

