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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 latency, throughput, or data freshness matters.

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, well-structured instructional skill: it never wastes tokens, organizes its guidance into clean sections, and includes an explicit verification section with safety guardrails. Its main weakness is actionability — measurement and readback steps are named but not shown, leaving Claude to invent the how (e.g., timing commands, queue-state checks) without examples.

Suggestions

Add one or two executable readback examples under 'Verification' — e.g., an HTTP timing command with response-header freshness check (`curl -sS -o /dev/null -w '%{time_total}' ...`) and a one-liner for inspecting queue depth — so the verification steps are copy-paste ready instead of named.

Concretize 'measure each segment separately' in 'Map The Hot Path' with a short example measurement for one segment of the template path, showing the tool and the metric to record.

Close the workflow with an explicit feedback loop after verification: what to re-check and in what order when a readback shows stale data or degraded providers (e.g., re-run hot-path measurement, confirm cache invalidation, re-verify freshness before declaring the path ready).

DimensionReasoningScore

Conciseness

The body is lean with no concept explanations Claude already knows and no padding; every section (metrics split, hot-path template, ordered optimizations, verification, guardrails) earns its tokens, matching the anchor for assuming Claude's competence with every token earning its place rather than the trimmable-over-explanation anchor 4.

5 / 5

Actionability

The metric checklist and numbered optimization order are concrete, but the measurement and verification steps stop at the what — 'measure each segment separately' and 'HTTP timing and response headers' give no executable commands or snippets (e.g., a timing curl, a queue-inspection command), matching the incomplete-guidance/missing-key-details anchor rather than anchor 4's mostly-executable guidance.

3 / 5

Workflow Clarity

There is a coherent sequence (split metrics, map the hot path, measure each segment, apply optimizations in order, verify with live readbacks) with an explicit verification section and a guardrail against unmeasured claims, but no error-recovery loop (what to do when a readback or freshness check fails) — clear sequence with most checkpoints, not the full feedback-loop anchor 5.

4 / 5

Progressive Disclosure

No bundle files exist and none are needed: the skill is single-purpose, compact, and organized into six clearly headed sections with no monolithic bulk or buried references, satisfying the simple-skill guideline that well-organized sections with no external-reference needs score 5.

5 / 5

Total

17

/

20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: it states concrete optimize/verify actions with specific metrics, enumerates target domains, and closes with an explicit 'Use when' trigger clause. The only gaps are a few missing natural trigger synonyms (e.g., 'response time', 'performance') and slight breadth around throughput/caching that could collide with general performance-tuning skills.

DimensionReasoningScore

Specificity

The description enumerates concrete actions — 'tracking p50/p95/p99 latency, freshness age, and queue depth, mapping hot paths, and running live readbacks' — across a comprehensive list of domains (realtime dashboards, market data feeds, streaming agents, execution gateways, queues, caches), matching the anchor for multiple specific concrete actions with comprehensive coverage rather than the minor-gaps anchor below.

5 / 5

Completeness

It explicitly answers both what ('Optimize and verify latency-sensitive systems... by tracking... mapping hot paths, and running live readbacks') and when ('Use when p95 latency, throughput, or data freshness matters') with concrete trigger phrases — an exact anchor-5 match, not the could-be-more-explicit anchor 4.

5 / 5

Trigger Term Quality

'p95 latency, throughput, or data freshness' plus 'realtime dashboards, market data feeds, streaming agents' are natural phrases users would say, but common variations like 'performance', 'response time', 'slow', or 'HFT' are absent — good coverage with a few natural terms missing rather than comprehensive synonym coverage.

4 / 5

Distinctiveness Conflict Risk

Terms like 'p50/p95/p99 latency', 'freshness age', 'queue depth', and 'execution gateways' carve out a distinct latency-engineering niche, but 'throughput' and 'caches' create minor overlap risk with general performance or benchmarking skills — mostly distinct with minor overlap, not the minimal-conflict anchor 5.

4 / 5

Total

18

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository

affaan-m/ECC

Path

skills/latency-critical-systems/SKILL.md

Reviewed

Table of Contents

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