tracking-application-response-times

This skill enables Claude to track and optimize application response times. It uses the response-time-tracker plugin to monitor API endpoints, database queries, external service calls, frontend rendering, and background job execution. The plugin calculates P50, P95, and P99 percentiles, average and maximum response times. Use this skill when you need to identify performance bottlenecks, monitor SLOs, or receive alerts about performance degradation. Trigger this skill with phrases like "track response times", "optimize latency", or "monitor application performance".

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill tracking-application-response-times

What are skills?

Review — 60%

Does it follow best practices?

If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:

npx tessl skill review --optimize ./path/to/skill

Learn more

Evaluation — 100%

Agent success when using this skill

Validation — 9 / 11 Passed

Validation for skill structure

SKILL.md

Review

Evals

Evaluation results

100%

API Performance Analysis Tool

Response time percentile metrics

Criteria

Without context

With context

P50 reported

100%

P95 reported

100%

P99 reported

100%

Average reported

100%

Maximum reported

100%

Per-endpoint breakdown

100%

All three endpoints present

100%

Script reads CSV

100%

Without context: $0.1850 · 33s · 9 turns · 10 in / 2,308 out tokens

With context: $0.3829 · 1m 24s · 18 turns · 67 in / 4,995 out tokens

100%

Performance Monitoring Setup for a Web Application

Multi-layer monitoring coverage

Criteria

Without context

With context

API endpoint tracking

100%

Database query tracking

100%

External service call tracking

100%

Background job tracking

100%

Per-operation granularity

100%

Monitoring plan covers all layers

100%

No proprietary dependencies

100%

Timing data recorded

100%

Without context: $0.2287 · 1m 4s · 8 turns · 9 in / 4,010 out tokens

With context: $0.3902 · 1m 33s · 17 turns · 66 in / 5,709 out tokens

100%

Diagnosing a Performance Regression

Alerting and root cause correlation

Criteria

Without context

With context

Alert thresholds present

100%

Baseline-derived thresholds

100%

Deviation-based alerting

100%

Per-endpoint alert rules

100%

CPU correlation

100%

Memory correlation

100%

Additional metric correlation

100%

Root cause identified

100%

analyze.py reads both files

100%

Without context: $0.4696 · 2m 6s · 14 turns · 15 in / 8,471 out tokens

With context: $0.6329 · 2m 55s · 24 turns · 21 in / 10,721 out tokens

Evaluated: about 2 hours ago
Agent: Claude Code

Table of Contents

API Performance Analysis Tool Performance Monitoring Setup for a Web Application Diagnosing a Performance Regression

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.