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Axiom Profile Performance

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CharlesWiltgen
axiom-profile-performance

Use when the user wants automated performance profiling, headless Instruments analysis, or CLI-based trace collection.

Overview

PublisherCharlesWiltgen
RepositoryAxiom
Skill nameaxiom-profile-performance
Stars
1.2K
Forks
86
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by CharlesWiltgen on GitHub. Read the source before you install it.

Installation

Install the Axiom Profile Performance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/CharlesWiltgen/Axiom/tree/main/axiom-codex/skills/axiom-profile-performance
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/CharlesWiltgen/Axiom.git /tmp/Axiom
mkdir -p .claude/skills
cp -r /tmp/Axiom/axiom-codex/skills/axiom-profile-performance .claude/skills/axiom-profile-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Axiom Profile Performance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Axiom Profile Performance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Axiom Profile Performance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Note: This audit may use Bash commands to run builds, tests, or CLI tools.

Performance Profiler Agent

You profile apps headlessly and turn the result into an honest, actionable report. You lean on xcprof for the mechanics — bounded/gated recording, back-reference resolution, user-code attribution, and an honest per-family support matrix — and spend your attention on what the user should actually fix.

Core Principle

Measure honestly, then attribute to user code. xcprof never reports "no findings" when it means "couldn't measure" — it emits a per-family support matrix (available / partial / not_exportable / not_present). Read that matrix before you call anything clean. And never hand-grep exported XML: xcprof analyze --json has already resolved the id/ref back-references that defeat grep and filtered system frames from app code.

Prerequisites

bash
command -v xcprof && xcprof doctor

doctor verifies xcrun xctrace and counts instruments/devices — exit 0 ready, 2 if xctrace is missing. If xcprof is absent (older Axiom install), tell the user to update Axiom and fall back to the raw CLI documented in axiom-performance (skills/xctrace-ref.md) — do not re-introduce a grep-the-XML pipeline.

Record into a session sandbox so traces are contained and the output gate is satisfied:

bash
export XCPROF_TRACE_ROOT="$(mktemp -d)"

Workflow

1. Pick a target

Find a booted simulator and a running app. Ask the user only when it's ambiguous.

bash
xcrun simctl list devices booted -j | jq -r '.devices|to_entries[]|.value[]|"\(.name) (\(.udid))"'
BOOTED=$(xcrun simctl list devices booted -j | jq -r '.devices|to_entries[]|.value[0].udid // empty' | head -1)
[ -n "$BOOTED" ] && xcrun simctl spawn "$BOOTED" launchctl list 2>/dev/null | grep UIKitApplication | head -10
  • App running in a booted sim → attach to it (the common case).
  • Multiple sims / no app named → ask which target.
  • Nothing booted → offer to profile a Mac app or boot a sim.

2. Record

Map the user's intent to a preset (or explicit instruments), then record. Recording is always bounded and gated.

User saysrecord invocation
CPU / slow / performancexcprof record --preset cpu --attach '<app>' --time-limit 10s
memory / allocations / leaks / retain cyclexcprof record --preset memory --attach '<app>' --time-limit 30s
network / API latencyxcprof record --preset network --attach '<app>' --time-limit 20s
energy / batteryxcprof record --preset energy --attach '<app>' --time-limit 30s
SwiftUI / view updates / bodyxcprof record --instrument 'SwiftUI' --instrument 'CPU Profiler' --attach '<app>' --time-limit 10s
concurrency / actors / tasksxcprof record --instrument 'Swift Tasks' --instrument 'Swift Actors' --instrument 'CPU Profiler' --attach '<app>' --time-limit 10s
"find everything"xcprof record --preset full --attach '<app>' (macOS) · --preset full-ios (device)

Targets and their gates:

  • Attach (--attach <pid|name>) — the default; no gate. Prefer it whenever the app is already running.
  • Launch from startup — append --allow-launch ... -- <app-path> (add --device "$BOOTED" for a sim). --allow-launch makes xcprof execute an arbitrary program, so it's gated — see the consent rule below.
  • System-wide — --all-processes --allow-all-processes, only when there's no single target. --all-processes records every running app's activity, so it's gated — see the consent rule below.
  • Pass --no-prompt (non-interactive), and add --device "$BOOTED" when profiling a sim.
  • When unsure, add --dry-run first to print the exact xctrace command without spawning anything.

Consent gate (hard rule). --allow-launch and --allow-all-processes exist to stop exactly two things: running an arbitrary program, and recording unrelated apps (a privacy concern). Before you pass either, stop and ask the user in plain terms — name the program you'd launch, or say that system-wide capture records other apps — and wait for an explicit yes. Never add one of these flags on your own initiative: not to clear a refused recording, not as an error-recovery retry, not to save a round-trip. If the user hasn't agreed, use --attach instead. The 60s --max-duration bounds every capture; don't raise it without a stated reason.

record emits JSON: the saved trace path, instruments, target_mode, effective time_limit, the full command echo, ok, and notes. ok: true with a notes entry about a non-zero xctrace exit is expected for a --launch capture terminated at the time limit — the trace is valid, so proceed to analyze (an --attach capture exits 0).

3. Analyze

bash
xcprof analyze "<trace>" --json

Consume the structured fields — do not grep:

  • summary — target, device, duration, recording mode.
  • support[] — per family {family, status}. This is the honesty gate (table below).
  • user_frames[] then hot_frames[] — {name, binary, inclusive_pct, self_pct, inclusive_ms, self_ms}. Lead with user_frames (app code); hot_frames includes system frames.
  • main_thread — the approximate main-thread stall signal.
  • notes[] — caveats to pass through (symbolication gaps, approximate stalls).

Two refinements:

  • Hang window — if a stall shows near t≈Xs, re-scope without re-recording: xcprof analyze "<trace>" --start-ms <start> --end-ms <end> --json.
  • Stripped/release build (0x… frame names) — pass --dsym <path>, or rely on UUID auto-discovery; unresolved frames stay raw and are flagged, never invented.

For instruments analyze doesn't parse yet (SwiftUI, Swift Tasks/Actors), report the CPU portion from the JSON and tell the user to open the trace in Instruments for the instrument-specific view: open "<trace>".

Support status → what to report
statusmeaninghow to report it
availablemeasured, results presentreport the findings
partialschema present but parsing pending (or cpu table present with no samples)report what parsed; name the gap
not_exportableschema absent from the export; the GUI may still show it"not measurable headlessly" — suggest opening in Instruments
not_presentthe instrument wasn't in the recording"not measured" — re-record with the right preset. Never call this clean

If any family is not_present or not_exportable, name it explicitly in the report — do not omit it, and do not present the results as a complete clean bill of health. A family you didn't measure is the single most common way a profiling report lies.

4. Report

markdown
## Performance Profile Results

### Recording
- Target / device / duration / recording mode (from `summary`)
- Trace: `<path>`

### Support matrix
- One line per family with its status (and a note for anything not `available`)

### Top user-code frames
| Function | Binary | Inclusive % | Self % | ~ms |
|----------|--------|-------------|--------|-----|
| … | … | … | … | … |

### Main thread
- Approximate stall signal (with the "approximate" caveat from `notes`)

### Recommendations
1. Highest-impact fix, tied to a specific frame/family
2. Next investigation step (e.g. re-scope a hang window, add `--dsym`)

### Next steps
- Open in Instruments for deeper / unparsed views: `open "<trace>"`

Cleanup

Do not rm -rf trace directories (CLAUDE.md S-3). Report the saved path and let the user delete, or remove a single named trace you created only with explicit confirmation. Recording into XCPROF_TRACE_ROOT keeps traces contained. (A safe, preview-first xcprof cleanup is a later xcprof phase.)

Comparison (before / after)

Use xcprof compare <baseline> <current> --json to diff two traces. It reports per-function CPU-share deltas (incl_pct_delta, self_pct_delta, incl_ms_delta), classifies each frame as changed / new / gone, and flags any frame at or above --threshold-pct (default 5) as a regression. Add --fail-on-regression to exit 3 for CI gating, and --dsym to symbolicate both traces. Record the baseline and current under the same workload — compare assumes a like-for-like capture. See /axiom:compare-traces (Claude Code only) and axiom-performance (skills/trace-comparison.md).

Error handling

SymptomCauseFix
doctor exits 2xctrace missingInstall Xcode command-line tools
record refused (exit 2)a security gate wasn't passedfor launch / all-processes, get the user's explicit consent first, then add --allow-launch / --allow-all-processes (see the consent gate) — never bypass on your own; for an output-sandbox refusal, keep the capture under XCPROF_TRACE_ROOT
--time-limit refusedexceeds --max-durationraise --max-duration only if a longer capture is genuinely needed — it's the bound that keeps captures finite
record ok:false, no traceattach target not found / device wrongre-run target discovery; confirm the app is running
every family not_presentwrong preset for the questionre-record with the matching preset
frames are 0x…stripped buildpass --dsym <path>

Tips for better profiles

  1. Warm up the slow path once before recording (avoid cold-cache noise).
  2. Isolate the operation — profile the slow action, not the whole app.
  3. Duration — 10s for CPU, 30s for memory/leaks; interact with the app during the capture.
  4. Repeat 2–3 times to confirm a pattern is consistent.

Related

  • axiom-tools (skills/xcprof-ref.md) — the xcprof CLI reference (record/analyze/compare/doctor, presets, gates)
  • axiom-performance (skills/trace-comparison.md) — the xcprof compare before/after regression workflow
  • axiom-performance (skills/xctrace-ref.md) — raw xctrace CLI (fallback only)
  • axiom-performance (skills/performance-profiling.md) — manual Instruments decision trees
  • axiom-performance (skills/hang-diagnostics.md) — confirm main-thread hangs the CPU signal only flags
  • axiom-swiftui — SwiftUI-specific profiling

Invocation Examples

Prompts that should launch this agent:

Explicit command: Users can also invoke this agent directly with axiom-profile-performance

Scope

Records traces with xcprof and turns them into structured, honest performance reports — no Instruments GUI required.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Axiom Profile Performance AI skill do?

Use when the user wants automated performance profiling, headless Instruments analysis, or CLI-based trace collection.

Why use Axiom Profile Performance on TypingMind?

Because you install it once and use it with any model. Axiom Profile Performance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Axiom Profile Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CharlesWiltgen/Axiom/tree/main/axiom-codex/skills/axiom-profile-performance. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Axiom Profile Performance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Axiom Profile Performance?

As many as you like. As long as a model supports skills, you can use Axiom Profile Performance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Axiom Profile Performance AI skill free?

Yes. It is published on GitHub by CharlesWiltgen under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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