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Performance hotspot check

Skill vishnujchandran/.agents/skills/performance-hotspot-check

A curated collection of reusable AI agent skills, playbooks, and prompts for security audits, engineering workflows, and automation.

Install
npx -y skills add vishnujchandran/.agents --skill performance-hotspot-check

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What its author says it does

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Evidence-first performance analysis skill that identifies high-impact bottlenecks in app code, database access, and API calls with safe, minimal-diff optimization plans.

SKILL.md

2.1 KB, 425 tokens by cl100k_base, as published. Nobody here has run it

performance-hotspot-check (v2.0)

Find real bottlenecks and optimize highest-impact hotspots first.

Trigger Signals

Use when user asks to:

  • find slow app/API paths
  • diagnose latency spikes
  • optimize DB/API-heavy workflows
  • reduce response time, compute cost, or build/runtime overhead

Modes

  • analyze-only (default): bottleneck report + optimization plan
  • patch-safe: apply low-risk optimizations after user confirmation

Baseline First (Required)

Capture current metrics before recommending fixes:

  • latency (p50/p95/p99 where possible)
  • DB query count/time for hot requests
  • external API call count/time
  • CPU/memory symptoms if available

What It Checks

  • Repeated expensive computation on hot paths
  • N+1 query patterns and missing indexes
  • Unbounded pagination/filtering/sorting
  • Serial external calls that can be reduced/batched/cached
  • Over-fetching and heavy payload construction
  • Blocking sync operations in latency-sensitive paths

Workflow

  1. Identify critical user/API paths and frequency.
  2. Collect baseline metrics and evidence.
  3. Locate bottlenecks in code, DB, and external calls.
  4. Rank by impact (latency × frequency × cost).
  5. Propose minimal, low-risk optimizations.
  6. Define before/after verification plan.

Output Contract

Return exactly:

  1. Hotspot summary with baseline evidence
  2. Findings table: priority | area | file | bottleneck | impact | fix
  3. Top 5 optimizations first
  4. Measurement plan (before/after metrics)
  5. Rollback notes for risky changes (if any)

Priority

  • P0: major user-facing latency/cost bottlenecks
  • P1: medium-impact inefficiencies on common paths
  • P2: low-impact cleanup opportunities

Guardrails

  • No optimization without evidence
  • Never trade correctness/security for speed
  • Prefer minimal diffs and reversible changes
  • Call out assumptions explicitly when profiling data is incomplete

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