coding-performance

A guide for improving a measured software bottleneck, such as response time, throughput, memory use, input/output, database cost, bundle size, or other resource consumption. It requires comparing a representative baseline with the result after the change.

In plain words
What is it for?
Use it to profile and optimize a specific function, query, allocation, transfer, render, lock, serialization step, or external wait under a defined workload.
Why use it?
It prevents guesswork by requiring evidence about where the time or resources are actually spent. It also checks that an improvement does not simply move an unacceptable cost elsewhere or break correctness.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/evoelsewhere/evoflux/coding-performance
Any agent
npx skills add evoelsewhere/evoflux --skill coding-performance
Clone the repo
git clone --depth 1 https://github.com/evoelsewhere/evoflux

Made for: Claude Code, Codex.

Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 915 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00072 $0.00915
Opus 5 $0.00036 $0.00458
Sonnet 5 $0.00014 $0.00183
Haiku 4.5 $0.00007 $0.00092

Measured yesterday against content hash d3f59f88b63d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

coding-performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

app/agent/builtin_skills/coding-performance/SKILL.md · 85 lines

How it starts

The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Improve code performance

Optimize a measured bottleneck while preserving correctness and shifting no unacceptable cost elsewhere. Do not load bundled references when this skill activates.

Define the experiment

  1. Specify workload, data scale, concurrency, environment, warmup, cache state, user-visible metric, baseline distribution, target, and correctness invariants.
  2. Reproduce under representative release conditions. Separate application cost from network, dependency, background-load, and measurement noise.
  3. Read references/measurement-protocol.md before comparing results when samples are noisy, caching matters, tail latency is material, memory grows over time, or environments differ.

Attribute before editing

Profile the workload and identify the exact call path, query, allocation, transfer, render, lock, serialization step, or external wait that owns the material cost. A hot function is not necessarily the optimization boundary; confirm how often it runs and whether its work is avoidable.

If profiling exposes only a query label, trace name, route, allocation text, or source fragment, call code_context with action="search" once to locate the owning declaration. Skip search when the profiler already reports an exact declared symbol.

After profiling exposes an exact symbol, use code_context to bound its structural context: callers for invocation sites, callees for delegated work, and references for dispatch/registration uses. Start at depth 1 and do not infer frequency, timing, allocation, or runtime order from static edges.

Keep refresh=true for the first indexed query and after edits. Use refresh=false only for an immediate follow-up that intentionally reuses the same index version.

Read references/code-context-contract.md only after a graph result exposes ambiguity, cross-repository traversal, or index limitations. Once profiling selects the exact symbol, make the graph the next structural observation; do not return to broad source discovery first.

Read the full file on GitHub · 85 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 85 lines · 72 tokens per session scan A d3f59f88b63d

Subscribe to this mod's changes

coding-performance is a skill published in the GitHub repository evoelsewhere/evoflux (5 stars, last pushed 5d ago), licensed Apache-2.0. It adds 72 tokens to every session and 915 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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