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DongDuong2001DongDuong2001/pudo-code-system/skills/debug/performance/SKILL.md

performance-debug

Skill5 starsChanged 5 months agoMITView on GitHub

Diagnosing and resolving performance bottlenecks and latency issues

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When the application is responding slower than SLAs allow.

    line 11
  2. Distinctive:

    When CPU usage is unexpectedly high.

    line 12
  3. Distinctive:

    When identifying database query bottlenecks (N+1 queries, missing indexes).

    line 13
  4. Distinctive:

    Measure First: Do not guess. Use profilers, APMs (Datadog, New Relic), and logging to find the bottleneck.

    line 16
  5. Distinctive:

    Algorithmic Complexity: Check for accidental O(n^2) or worse loops.

    line 18
  6. Distinctive:

    Caching: Consider if a caching layer (Redis, Memcached) can bypass the heavy computation.

    line 19

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Performance Debugging Skill

This skill focuses on identifying, analyzing, and resolving performance-related issues such as slow response times, CPU spikes, and UI lag.

When to use this skill

  • When the application is responding slower than SLAs allow.
  • When CPU usage is unexpectedly high.
  • When identifying database query bottlenecks (N+1 queries, missing indexes).

Guidelines

  • Measure First: Do not guess. Use profilers, APMs (Datadog, New Relic), and logging to find the bottleneck.
  • Isolate the Scope: Determine if the issue is in the frontend, network, backend, or database layer.
  • Algorithmic Complexity: Check for accidental O(n^2) or worse loops.
  • Caching: Consider if a caching layer (Redis, Memcached) can bypass the heavy computation.

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