Latency Critical Systems
When you're building realtime dashboards, market data feeds, or anything where p95 latency actually matters, this skill keeps Claude focused on the hot path. It pushes you to split metrics properly (p50/p95/p99, throughput, freshness age, queue depth), map the entire chain from event to user-visible state, and verify with live readbacks instead of guessing. The optimization order is pragmatic: remove round trips, cache with freshness metadata, batch calls, then move compute closer to data. It won't let you hide stale data behind fast cache hits or claim millisecond behavior without measurement, which is exactly the discipline you need when latency is user-facing.
- skills.sh installs
- 5.3K
- GitHub stars
- 300.3K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add affaan-m/ecc --skill latency-critical-systems --agent claude-codeInstalls into .claude/skills of the current project.
Files
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
Sponsors
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- Repository
- github.com/affaan-m/ecc
- Category
- Cloud & Infrastructure
- First seen
- Jul 14, 2026
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