Performance Profiling

What it does

When your agent uses it

• Investigating app hangs
• App Store performance readiness
• Adding ossignpost and measurement hooks

Example prompts

• “/performance-profiling”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Identify the performance category from the user report, traces, logs, or code path.
2. 2Read only the matching reference file unless the issue is broad or unclear.
3. 3Prefer real device profiling with a Release build and representative data.
4. 4Inspect the code path named by the profile before proposing a fix.
5. 5Apply the smallest targeted fix that addresses the measured bottleneck.
6. 6Re-profile or add a repeatable measurement to confirm the improvement.

What it can do on your machine

Read from SKILL.md and the folder at commit 238ef60. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are swift).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performance Profiling loads about 1.5k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 87 tokens; SKILL.md has 502 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~87

When it runs · the whole SKILL.md, loaded when a task matches

~1.5k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nirholas/three.ws at commit 238ef60, republished under its Apache-2.0 licence (© nirholas). 502 words, ~1,540 tokens.

name

performance-profiling

description

Guide performance profiling for Apple platform apps with Instruments, Xcode diagnostics, and MetricKit. Use when investigating app hangs, stutters, high CPU, memory leaks, memory growth, OOM crashes, slow launch, battery drain, thermal issues, App Store performance readiness, or when adding os_signpost and measurement hooks.

What performance problem are you investigating?

+ App hangs, stutters, dropped frames, slow UI, high CPU
  -> Read references/time-profiler.md

+ High memory, leaks, OOM crashes, growing footprint
  -> Read references/memory-profiling.md

+ Slow cold launch, warm launch, resume, or time to first frame
  -> Read references/launch-optimization.md

+ Battery drain, thermal throttling, background energy, network waste
  -> Read references/energy-diagnostics.md

+ General "app feels slow"
  -> Start with references/time-profiler.md, then references/memory-profiling.md

+ Pre-release performance audit
  -> Read all reference files and use the review checklist below

import MetricKit

final class PerformanceReporter: NSObject, MXMetricManagerSubscriber {
    func startCollecting() {
        MXMetricManager.shared.add(self)
    }

    func didReceive(_ payloads: [MXMetricPayload]) {
        for payload in payloads {
            if let launch = payload.applicationLaunchMetrics {
                log("Resume time: \(launch.histogrammedResumeTime)")
            }
            if let responsiveness = payload.applicationResponsivenessMetrics {
                log("Hang time: \(responsiveness.histogrammedApplicationHangTime)")
            }
            if let memory = payload.memoryMetrics {
                log("Peak memory: \(memory.peakMemoryUsage)")
            }
        }
    }

    func didReceive(_ payloads: [MXDiagnosticPayload]) {
        for payload in payloads {
            if let hangs = payload.hangDiagnostics {
                for hang in hangs {
                    log("Hang: \(hang.callStackTree)")
                }
            }
        }
    }
}

© nirholas, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in third_party/designcode-agent-skills/agent-skills/codex/performance-profiling of nirholas/three.ws.

• SKILL.md
• agents/openai.yaml
• demo/PROMPT.md
• demo/expected-output.md
• demo/index.html
• demo/input.md
• demo/preview.jpg
• references/energy-diagnostics.md
• references/launch-optimization.md
• references/memory-profiling.md
• references/time-profiler.md

Open the folder on GitHubat commit 238ef60

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in nirholas/three.ws, which our catalogue first saw on October 7, 2026.

• MengTo/Skillsagent-skills/codex/performance-profiling