Agent-Skills.md

Agent Skills: Hotspot optimizer

Finds algorithmic complexity and performance hotspots, then optimizes them behind tests without changing behavior. Use for nested loops, repeated scans, N+1 queries, costly re-renders, or when a request mentions hot paths or slow code. Defaults to an analysis-only report.

UncategorizedID: bjornmelin/dev-skills/hotspot-optimizer

Repository

bjornmelin/dev-skills
BjornMelin
8

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pnpm dlx add-skill https://github.com/BjornMelin/dev-skills/tree/HEAD/plugins/claude-core/skills/hotspot-optimizer

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plugins/claude-core/skills/hotspot-optimizer/SKILL.md

Skill Metadata

Name
hotspot-optimizer
Description
"Finds algorithmic complexity and performance hotspots, then optimizes them behind tests without changing behavior. Use for nested loops, repeated scans, N+1 queries, costly re-renders, or when a request mentions hot paths or slow code. Defaults to an analysis-only report."

Hotspot optimizer

Find and (on request) fix complexity/performance hotspots with behavior preserved and proven by tests. Bias toward small, verified wins over broad rewrites.

Core rule

Never change observable behavior. Every optimization preserves outputs, ordering, error semantics, and public APIs — and is proven by a test that passed before and after.

Loop: Triage → Prove → Optimize → Verify

  1. Triage — get candidate leads fast, then reason about them:
    • First pass (cheap, optional): python3 scripts/scan_hotspots.py <root> --format json for a ranked multi-language lead list. Treat output as leads, not proof.
    • Confirm with Grep/Glob/Read: nested loops over the same data, membership tests in a loop (list vs set), sort-in-loop, pairwise comparisons, repeated scans, render recomputation, N+1 queries. Rank by impact (hot path × input size), not raw count.
  2. Prove — before touching hot code, run/author a focused test that pins current behavior and, if feasible, a quick timing or complexity witness.
  3. Optimize — apply the smallest localized Edit that lowers complexity (see references/optimization-playbook.md). No drive-by refactors or formatting churn.
  4. Verify — re-run the focused test, then the broad gates (type-check, lint, build, suite). If any regresses, revert that change.

Subagent fan-out

For large repos, shard files/dirs across Task subagents (read-only). Each returns a ranked findings list: file:line, pattern, estimated current → target complexity, risk. The main agent dedupes and merges into one ranked plan, then optimizes serially (it owns all edits). Cap concurrency at ~4-6 subagents; prefer fewer, broader shards over many tiny ones.

Optimization safety checklist

  • Same outputs for the same inputs (including ordering and ties).
  • No swallowed/altered exceptions; same error types.
  • No new unbounded memory (a set/dict cache must be bounded by the same data).
  • Equivalent for empty / single / duplicate / large inputs.
  • Public API and call sites unchanged.

Analysis-only by default

Default to a report (see references/report-template.md): ranked findings with location, current and target complexity, risk, and a proposed fix — but do not edit. Apply fixes only when the user says implement / fix / apply / refactor (or pre-approves low-risk wins like list→set membership).

Resources

  • scripts/scan_hotspots.py — heuristic multi-language hotspot scanner (leads only).
  • references/optimization-playbook.md — transformation catalog + correctness checks + what-not-to-do.
  • references/report-template.md — analysis-only report skeleton.