
Heap Snapshot Analysis
OfficialFreeInvestigate V8 heap snapshots for memory issues.
Free · Opens the source repo
What Heap Snapshot Analysis does
Heap Snapshot Analysis is a specialized skill designed to help developers and engineers analyze V8 heap snapshots, which are crucial for identifying memory leaks and retention issues in JavaScript applications. This skill is activated when the user provides .heapsnapshot files, allowing for in-depth analysis of memory usage before and after specific workflows. It includes a suite of helpers for parsing snapshots, comparing them, and tracing the paths of retained objects, making it an essential tool for performance optimization in JavaScript environments.
The skill's functionality is built around several key operations. First, it allows users to parse large heap snapshot files efficiently, utilizing buffer-based extraction techniques to handle files that can exceed 500MB in size. For snapshots larger than 2 GiB, users can write streaming scripts to analyze only the relevant sections, ensuring that memory constraints do not hinder the analysis process. This is particularly useful for developers dealing with large-scale applications where memory management is critical.
In addition to parsing, the skill provides capabilities for comparing snapshots to identify changes in object counts and sizes, as well as for tracing retainer paths to understand why certain objects remain in memory. By using the provided helpers, users can write custom investigation scripts in the scratchpad directory, allowing for flexible and iterative analysis tailored to specific memory issues. This structured approach not only aids in identifying leaks but also in documenting the investigation process, which is vital for future reference and collaboration.
Overall, Heap Snapshot Analysis is targeted at developers and performance engineers who need a robust tool for diagnosing memory-related issues in JavaScript applications. Its focus on V8 heap snapshots makes it particularly relevant for those working within the Node.js ecosystem or any environment that relies on V8 for JavaScript execution.
When to use it
Use this skill when you have `.heapsnapshot` files from V8 and need to analyze memory usage, compare snapshots, or trace object retainers.
When not to use it
This skill is not suitable for general performance profiling or when no heap snapshots are available for analysis.
What you can build with it
Analyzing Memory Leaks
You have a `.heapsnapshot` file from a production environment and need to identify memory leaks in your application.
Comparing Object Counts
After making changes to your application, you want to compare the before and after heap snapshots to see how memory usage has changed.
Tracing Object Retainers
You suspect that a specific object is not being garbage collected and want to trace its retainer paths to understand why.
How to install Heap Snapshot Analysis
View source1. Install with the skills CLI
npx skills add microsoft/vscode/heap-snapshot-analysis --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by microsoftHeap Snapshot Analysis
Investigate memory leaks from V8 heap snapshots (.heapsnapshot files). This skill starts when snapshots already exist: either the user provided them, DevTools exported them, or another workflow produced them. Use the helpers here to compare snapshots, group object deltas, and trace retainer paths.
IGNORE Prior Investigations
Start every investigation fresh. Do NOT read, consult, or be influenced by prior investigations found in:
/memories/(user, session, or repo memory).github/skills/heap-snapshot-analysis/scratchpad/(previous dated subfolders and theirfindings.mdfiles)- Any other notes from earlier sessions
Previous findings can bias the analysis toward suspects that are no longer relevant, or cause the agent to skip steps and jump to conclusions. Let the current snapshots speak for themselves. Only reference prior work if the user explicitly asks you to.
When to Use
- User provides
.heapsnapshotfiles (before/after a workflow) - User has heap snapshots captured by another skill or script
- Need to find what retains disposed objects (retainer path analysis)
- Comparing object counts/sizes between two snapshots
- Investigating why particular objects survive GC
Workflow
If the user needs the agent to launch VS Code, drive a scenario, and capture snapshots first, use the VS Code performance workflow skill before returning here for low-level snapshot analysis.
1. Parse Snapshots
Use the helpers in parseSnapshot.ts to load snapshots. The files are often >500MB and too large for JSON.parse as a string — the helpers use Buffer-based extraction. In scratchpad scripts, import helpers from ../helpers/*.ts.
For very large snapshots, the helper may still be too eager. Node cannot create a Buffer larger than roughly 2 GiB, so snapshots above that size can fail with ERR_FS_FILE_TOO_LARGE even before parsing. In that case, do not try to raise --max-old-space-size and retry the same full-file read. Switch to a streaming script.
import { parseSnapshot, buildGraph } from '../helpers/parseSnapshot.ts';
const data = parseSnapshot('/path/to/snapshot.heapsnapshot');
const graph = buildGraph(data);
Snapshots Larger Than 2 GiB
When a snapshot is too large to load into a single Buffer, write scratchpad scripts that scan and parse only the sections needed for the question. Use streamSnapshot.mjs for the common streaming primitives instead of copying them between scratch scripts.
Useful tricks:
- Find top-level section offsets first. Scan the file as bytes for markers like
"nodes":,"edges":,"strings":, and"trace_function_infos":. This lets follow-up scripts jump directly to the large arrays instead of searching the whole file repeatedly. - Parse
snapshot.metaseparately from the small header at the start of the file. Usemeta.node_fields,meta.node_types,meta.edge_fields, andmeta.edge_typesto avoid hard-coding tuple widths. - Stream numeric arrays in chunks. For
nodesandedges, keep a small carryover string between chunks, split on commas, and process complete numeric tokens as they arrive. - Avoid materializing the full
stringstable unless the investigation truly needs it. If you only need suspicious names, collect string indexes from matching nodes/edges first, then resolve only those indexes in a second streaming pass. - If you do need many strings, store only short previews and category counters. Full source strings, ref-listing strings, and prompt payloads can dominate memory and make the analyzer become the leak.
- Write intermediate outputs to files in the scratchpad. Large heap analysis is iterative and slow; cached node ids, offsets, and retainer traces save repeated multi-minute passes.
- Prefer self-size attribution and field-level ownership for huge graphs. Full retained-size walks can wildly overcount shared services, roots, maps, and singleton caches.
- When quantifying a suspected owner, count obvious owned fields separately: wrapper object, key arrays, array elements, direct strings, and parent strings of sliced/concatenated strings. This often gives a better lower-bound than a single direct string bucket.
- Be explicit about approximation boundaries. A field-level subtotal usually undercounts listeners/watchers/back-references but avoids the much worse problem of attributing the whole runtime to one object.
Example large-snapshot workflow:
import { findArrayStart, findTokenOffsets, parseMeta, streamNumberTuples } from '../../helpers/streamSnapshot.mjs';
const { size, offsets } = findTokenOffsets(snapshotPath);
const meta = parseMeta(snapshotPath);
const nodeFieldCount = meta.node_fields.length;
const nodesStart = findArrayStart(snapshotPath, offsets.get('"nodes"'));
streamNumberTuples(snapshotPath, nodesStart, offsets.get('"edges"'), nodeFieldCount, (node, nodeIndex) => {
// node is reused for speed; copy it before storing.
});
cd .github/skills/heap-snapshot-analysis
node --max-old-space-size=24576 scratchpad/YYYY-MM-DD-topic/findOffsets.mjs /path/to/Heap.heapsnapshot
node --max-old-space-size=24576 scratchpad/YYYY-MM-DD-topic/streamAnalyze.mjs /path/to/Heap.heapsnapshot > scratchpad/YYYY-MM-DD-topic/streamAnalyze.out
node --max-old-space-size=24576 scratchpad/YYYY-MM-DD-topic/traceNodes.mjs /path/to/Heap.heapsnapshot 12345 67890 > scratchpad/YYYY-MM-DD-topic/traceNodes.out
2. Compare Before/After
Use compareSnapshots.ts to diff two snapshots:
import { compareSnapshots } from '../helpers/compareSnapshots.ts';
const result = compareSnapshots('/path/to/before.heapsnapshot', '/path/to/after.heapsnapshot');
// result.topBySize, result.topByCount, result.newObjectGroups, result.summary
3. Find Retainer Paths
Use findRetainers.ts to trace why an object is alive:
import { findRetainerPaths } from '../helpers/findRetainers.ts';
// Find what keeps ChatModel instances alive (skipping weak edges)
findRetainerPaths(graph, 'ChatModel', { maxPaths: 5, maxDepth: 25, maxAttempts: 200 });
4. Write Investigation Scripts
Write investigation-specific scripts in the scratchpad directory. This folder is gitignored — use it freely for one-off analysis.
Organize scratchpad work into dated subfolders named YYYY-MM-DD-short-description/ (e.g., 2026-04-09-chat-model-retainers/). Each subfolder should contain:
- The analysis scripts (
.mjs,.mts, etc.) - A
findings.mdfile documenting the full investigation: all ideas considered, which ones led to changes and which were rejected (and why), before/after measurements, and a summary of the outcome. This lets the user review the agent's reasoning, decide which changes to keep, and follow up on deferred ideas.
Scripts can import the helpers:
cd .github/skills/heap-snapshot-analysis
node --max-old-space-size=16384 scratchpad/2026-04-09-chat-model-retainers/analyze.mjs
Key Concepts
V8 Heap Snapshot Format
The .heapsnapshot file is JSON with these key sections:
snapshot.meta: Field definitions for nodes and edgesnodes: Flat array, every N values = one node (N =meta.node_fields.length, typically 6:type, name, id, self_size, edge_count, detachedness)edges: Flat array, every M values = one edge (M =meta.edge_fields.length, typically 3:type, name_or_index, to_node)strings: String table indexed bynamefields in nodes/edges
Edge Types That Matter
| Type | Meaning | Prevents GC? |
|---|---|---|
property | Named JS property | Yes |
element | Array index | Yes |
context | Closure variable | Yes |
internal | V8 internal reference | Yes |
hidden | V8 hidden reference | Yes |
weak | WeakRef/WeakMap key | No |
shortcut | Convenience link | Depends |
Always skip weak edges when tracing retainer paths. WeakMap entries show up as edges from key → backing array, but they don't prevent collection — they're red herrings.
Common VS Code Retention Patterns
-
RowCache templates: ListView's
RowCachestores template rows. Templates havecurrentElementpointing to old viewmodel items. If not cleared on session switch, retains entire model chains. -
Resource pools:
pool.clear()only disposes idle items. If_onDidUpdateViewModel.fire()runs AFTERpool.clear(), released items re-enter the empty pool and are never disposed. Fire event first, then clear. -
autorunIterableDeltalastValues: The closure captures aMapof previous iteration values. Values stay until the autorun re-runs. Async disposal delays keep models in observable stores longer than expected. -
HoverService._delayedHovers: Global singleton Map retaining disposed objects viashowclosure →resolveHoverOptionsclosure →this. If hover cleanup disposable doesn't fire, the entire object tree is retained. -
ObjectMutationLog._previous: The incremental serializer keeps a full snapshot of the last-serialized state. Every loaded ChatModel holds 2x its data: live +_previous. -
_previousModelRefpattern:MutableDisposablesetter disposes the old value. Reading.valueand storing it elsewhere, then setting.value = undefined, disposes the stored reference. UseclearAndLeak()to extract without disposing.
Defensive Nulling
Null heavy fields in dispose() to break retention chains even when something retains the disposed object:
override dispose() {
super.dispose();
this._requests.length = 0; // conversation data
this.dataSerializer = undefined; // serialization snapshot
this._editingSession = undefined; // editing session + TextModels
this._session = undefined!; // back-reference cycles
}
Caveat: Don't null fields on viewmodel items (ChatResponseViewModel._model). The tree's diffIdentityProvider accesses them after the parent viewmodel is disposed but before setChildren replaces them.
False Retainers to Watch For
- DevTools debugger global handles: If the snapshot was captured after opening DevTools, large source strings, compiled scripts, preview data, inspected objects, or debugger bookkeeping can be retained by paths like
DevTools debugger(internal)→synthetic::(Global handles)→ GC roots. Treat these as debugger-induced until proven otherwise. They may not exist in the app before DevTools opens, and they should not be confused with application-owned leaks. DevToolsLogger._aliveInstances(Map): Enabled byVSCODE_DEV_DEBUG_OBSERVABLESenv var. Retains ALL observed observables. Check if this is active before investigating observable-rooted paths.GCBasedDisposableTracker(FinalizationRegistry): Ifregister(target, held, target)is used (target === unregister token), creates a strong self-reference preventing GC. Currently commented out in production.- WeakMap backing arrays: Show up in retainer paths but don't prevent collection.
Running Analysis
All helper scripts use ESM and need Node with extra memory:
node --max-old-space-size=16384 scratchpad/analyze.mjs
Typical analysis takes 30-120 seconds per snapshot depending on size.
Frequently asked questions about Heap Snapshot Analysis
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