latency-critical-systems
Use for latency-sensitive systems such as realtime dashboards, market data, streaming agents, execution gateways, queues, caches, or HFT-like infrastructure where freshness and p95 latency matter. Use when p95 latency or data freshness matters — realtime dashboards, market data, streaming agents, queues, or caches.
Source facts
- Repository
- affaan-m/ECC
- Last source activity
- August 12, 2026 at 03:58
- Detected SKILL.md language
- English
- Stars
- 264,820
- Forks
- 39,570
Install options
The review-first prompt is selected by default. You can switch to a direct command or download a local copy.
Review the source files
Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
Showing SKILL.md
SKILL.md
Source instructions · Read-only preview- name
- latency-critical-systems
- description
- Use for latency-sensitive systems such as realtime dashboards, market data, streaming agents, execution gateways, queues, caches, or HFT-like infrastructure where freshness and p95 latency matter. Use when p95 latency or data freshness matters — realtime dashboards, market data, streaming agents, queues, or caches.
- 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:
```text
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.
View on GitHub