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.
Install
$ agentstack add skill-affaan-m-ecc-latency-critical-systems ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-affaan-m-ecc-latency-critical-systems) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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 stateThen measure each segment separately.
Optimization Order
- Remove unnecessary round trips.
- Cache stable reads with freshness metadata.
- Batch small calls and writes.
- Move compute closer to the data or the user.
- Split hot and cold paths.
- Apply backpressure before queues grow unbounded.
- Use streaming only when it improves freshness or user experience.
- 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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: affaan-m
- Source: affaan-m/ECC
- License: MIT
- Homepage: https://ecc.tools
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.