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Ios Memgraph Analysis

CommunityPopular
dpearson2699
ios-memgraph-analysis

Use when capturing or analyzing an iOS .memgraph, especially when the task mentions a memory leak, heap growth, persistent memory increase, ownership path, or matched-capture comparison with Apple CLI tools. Covers unambiguous Simulator capture, leaks/heap/vmmap/malloc_history evidence, raw artifact preservation, and same-flow verification. Use debugging-instruments for interactive Xcode Memory Graph, Instruments, generic retain-cycle inspection, or LLDB work.

Overview

Publisherdpearson2699
Repositoryswift-ios-skills
Skill nameios-memgraph-analysis
Stars
1.2K
Forks
61
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by dpearson2699 on GitHub. Read the source before you install it.

Installation

Install the Ios Memgraph Analysis 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/dpearson2699/swift-ios-skills/tree/main/skills/ios-memgraph-analysis
  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/dpearson2699/swift-ios-skills.git /tmp/swift-ios-skills
mkdir -p .claude/skills
cp -r /tmp/swift-ios-skills/skills/ios-memgraph-analysis .claude/skills/ios-memgraph-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ios Memgraph Analysis 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 Ios Memgraph Analysis 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 Ios Memgraph Analysis 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.

iOS Memgraph Analysis

Use memory graphs to prove why memory survives a defined lifetime boundary. Separate unreachable leaks from reachable growth, preserve raw tool output, and verify the same app-owned type and ownership path after a fix.

Contents

Boundary

This skill owns .memgraph capture and command-line ownership/growth analysis. Use the Memory Graph Debugger or Instruments when their interactive graph and allocation timeline are the primary task. Use source review for a suspected closure capture only after runtime evidence identifies the lifetime or path.

Evidence Model

Do not collapse these conditions:

  • Unreachable leak: allocated memory no longer has a path from a live root. An isolated strong cycle can be unreachable and still consume memory.
  • Reachable but abandoned state: a live root still retains objects the user flow no longer needs. leaks may correctly report zero.
  • Expected cache or pool: memory survives intentionally and must be judged by its bound, eviction behavior, and pressure response.
  • Heap regression or fragmentation: footprint grows because more/larger allocations persist or dirty pages are poorly utilized, without a leak.

Apple's leak scanner uses conservative pointer discovery and incomplete type metadata. Counts can fluctuate, and a zero result does not prove the absence of an ownership bug. Strong evidence identifies the expected lifetime, an app-owned type or allocation, and a credible path or isolated reproduction.

Workflow

1. Define the lifetime before capturing

Name the object that should disappear and the event that ends its useful life. For example: EditorViewModel should deinitialize after dismissing the editor and completing pending save work.

Record one deterministic sequence:

  1. launch or restore a known state;
  2. take an optional baseline graph;
  3. perform the feature flow;
  4. cross the expected release boundary;
  5. wait for legitimate asynchronous cleanup;
  6. take the post-flow graph.

Keep build, simulator/device, data, Malloc Stack Logging setting, and repetition count stable. Malloc Stack Logging adds valuable allocation backtraces but also overhead; compare only runs with the same setting.

2. Capture a graph without guessing the process

Xcode can export a graph from the Memory Graph Debugger. For a running Simulator app, use the helper from this skill:

bash
mkdir -p /tmp/myapp-memory
mkdir /tmp/myapp-memory/run-01
python3 scripts/capture_sim_memgraph.py \
  --bundle-id com.example.MyApp \
  --output-dir /tmp/myapp-memory/run-01 \
  --pretty > /tmp/myapp-memory/run-01/capture.json

The per-run mkdir must fail if the capture directory already exists. Use a new run name rather than mixing stale evidence with a retry.

Pass --udid when more than one Simulator is booted. The helper accepts only one exact launchd label and PID; zero or multiple matches are errors. It runs the host leaks --outputGraph command, retains stdout/stderr, and writes a manifest. Do not replace this with pgrep | head -1 or a substring match.

Capturing suspends the process. Do not use capture latency as performance data.

3. Preserve raw output and build a bounded summary

bash
MEMGRAPH=$(jq -er \
  'select(.status == "captured") | .memgraph | select(type == "string" and length > 0)' \
  /tmp/myapp-memory/run-01/capture.json)
test -s "$MEMGRAPH"
python3 scripts/summarize_memgraph.py \
  "$MEMGRAPH" \
  --artifact-dir /tmp/myapp-memory/run-01/analysis-raw \
  --app-image 'MyApp|MyFeatureKit' \
  --trace-limit 3 --group-by-type --pretty \
  > /tmp/myapp-memory/run-01/analysis.json

Read the exact graph path from the preserved capture report; do not guess a timestamped filename. The helper creates a dedicated raw-artifact directory, refuses to reuse it, runs leaks --list, and parses only a conservative subset of its text. --app-image marks candidate rows; it does not prove ownership. --trace-limit runs bounded leaks --traceTree=<address> queries. Add --reference-tree when aggregate root paths are more useful than individual leaked addresses. With --group-by-type, that reference-tree query is grouped in the same invocation. Exit statuses 0 and 1 from leaks remain analyzable; a primary status above 1 fails the summary, while optional-query failures are preserved and warned as unusable without discarding a valid primary summary.

Apple does not publish these text formats as stable machine schemas. Treat parse warnings as a reason to inspect the raw artifacts, not to loosen the parser until it emits a desired answer.

4. Find the first actionable app-owned edge

Start with an app-owned leaked type or allocation stack. Inspect:

  • the leak's object graph and Malloc Stack Logging backtrace, when present;
  • a bounded --traceTree=<address> for objects that reference one address;
  • --groupByType to compress repeated types and reveal a retained payload;
  • --referenceTree for a top-down view when the responsible address is unclear;
  • source code for the first strong edge controlled by the app.

An unreachable self-cycle may have no live root in traceTree. Use the grouped leak graph plus source verification or reduce the behavior to an isolated reproduction. Never invent a root path that the graph does not contain.

5. Investigate growth when leaks is empty

Use matching baseline and post-flow graphs, locate the growing region, compare object types, then trace a suspicious address back to an app-owned edge. The evidence goal is persistent reachable growth across the same lifetime—not a lower RSS value or a single large snapshot. Load reachable-growth.md only for this empty-leak branch; it contains the ordered vmmap, heap, leaks, and malloc_history queries and their logging-dependent alternatives.

6. Fix and verify the same lifetime

Prefer the narrowest ownership correction: break the unintended strong edge, cancel work that owns the object, remove an observer, bound/evict a cache, or release a large buffer after its last use. Use weak when the reference may legitimately become nil; use unowned only with a proven lifetime guarantee.

Repeat the identical flow. A fix is supported when the same app-owned type/path disappears or the pre/post growth attributable to it is removed across repeated runs. Lower RSS, a smaller graph file, or a lower aggregate leak count alone is not proof.

Ownership Decisions

EvidenceNext action
App type in a root cycleInspect both strong edges and allocation stack.
No root for a leaked addressInspect grouped cycle evidence and isolate the flow.
Live root retains dismissed feature stateFollow the path to the first app-owned edge.
Zero leaks but repeated malloc growthDiff baseline/post heap objects.
Framework object dominatesFind the app-created owner, input, or call frequency.
Growth stabilizes at a documented boundTest eviction/pressure behavior before changing it.

Common Mistakes

  • Declaring the app leak-free because leaks returned zero once.
  • Selecting the first PID or Simulator from an ambiguous list.
  • Enabling Malloc Stack Logging in only one side of a comparison.
  • Treating a parser's best-effort type column as an API guarantee.
  • Pasting enormous reference trees into a report without finding an app edge.
  • Fixing every closure with [weak self] without reasoning about lifetime.
  • Claiming success from graph size, RSS, or total-count changes without proving the target lifetime and ownership path.

Review Checklist

  • The object and expected release boundary are explicit.
  • Baseline and post-flow graphs use the same build, runtime target, data state, deterministic flow, cleanup wait, repetitions, and Malloc Stack Logging setting.
  • Simulator, bundle identifier, process label, and PID are unambiguous.
  • Original graph and raw command outputs are preserved.
  • Current installed-tool help confirms version-sensitive command shapes.
  • Leak, reachable growth, expected cache, and fragmentation are separated.
  • The finding names an app-owned type/allocation and credible path.
  • Missing type metadata or conservative-scanner limits are disclosed.
  • The fix changes one ownership/lifetime cause.
  • Verification repeats the same flow and evidence query.

References

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 Ios Memgraph Analysis AI skill do?

Use when capturing or analyzing an iOS .memgraph, especially when the task mentions a memory leak, heap growth, persistent memory increase, ownership path, or matched-capture comparison with Apple CLI tools. Covers unambiguous Simulator capture, leaks/heap/vmmap/malloc_history evidence, raw artifact preservation, and same-flow verification. Use debugging-instruments for interactive Xcode Memory Graph, Instruments, generic retain-cycle inspection, or LLDB work.

Why use Ios Memgraph Analysis on TypingMind?

Because you install it once and use it with any model. Ios Memgraph Analysis 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 Ios Memgraph Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dpearson2699/swift-ios-skills/tree/main/skills/ios-memgraph-analysis. 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 Ios Memgraph Analysis?

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 Ios Memgraph Analysis?

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

Is the Ios Memgraph Analysis AI skill free?

It is published on GitHub by dpearson2699. Check the repository for licensing terms. 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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