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Agent Skill · 研究检索

heap-snapshot-analysis(堆快照分析)

heap-snapshot-analysis 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

heap-snapshot-analysis 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 49,433。

总安装 652周安装 28GitHub Stars 184,382下载 228核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:heap-snapshot-analysis(堆快照分析)
来源仓库:https://github.com/microsoft/vscode
仓库路径:skills/heap-snapshot-analysis
安装命令:
npx skills add https://github.com/microsoft/vscode --skill heap-snapshot-analysis
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/microsoft/vscode --skill heap-snapshot-analysis

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选堆快照分析相关资源。适合在内存调试或性能优化等场景中快速定位资料。可结合关键词进行定向检索和结果筛选。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。使用前应核实信息来源的权威性和时效性。

heap-snapshot-analysis 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/heap-snapshot-analysis

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/microsoft/vscode --skill heap-snapshot-analysi…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

heap-snapshot-analysis 怎么安装?

在终端执行 npx skills add https://github.com/microsoft/vscode --skill heap-snapshot-analysis。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

heap-snapshot-analysis 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

heap-snapshot-analysis 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

heap-snapshot-analysis 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

heap-snapshot-analysis 用的人多吗?

公开数据是累计安装 652 次,近一周安装 28 次,下载 228 次,GitHub 184,382 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

heap-snapshot-analysis 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 184,382 星,维护活跃度相对可查。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
heap-snapshot-analysis

堆快照分析 · 本页

652184,382只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Heap 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 their findings.md files)
  • 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 .heapsnapshot files (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.meta separately from the small header at the start of the file. Use meta.node_fields, meta.node_types, meta.edge_fields, and meta.edge_types to avoid hard-coding tuple widths.
  • Stream numeric arrays in chunks. For nodes and edges, keep a small carryover string between chunks, split on commas, and process complete numeric tokens as they arrive.
  • Avoid materializing the full strings table 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.md file 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 edges
  • nodes: 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 by name fields in nodes/edges

Edge Types That Matter

TypeMeaningPrevents GC?
propertyNamed JS propertyYes
elementArray indexYes
contextClosure variableYes
internalV8 internal referenceYes
hiddenV8 hidden referenceYes
weakWeakRef/WeakMap keyNo
shortcutConvenience linkDepends

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

  1. RowCache templates: ListView's RowCache stores template rows. Templates have currentElement pointing to old viewmodel items. If not cleared on session switch, retains entire model chains.
  2. Resource pools: pool.clear() only disposes idle items. If _onDidUpdateViewModel.fire() runs AFTER pool.clear(), released items re-enter the empty pool and are never disposed. Fire event first, then clear.
  3. autorunIterableDelta lastValues: The closure captures a Map of previous iteration values. Values stay until the autorun re-runs. Async disposal delays keep models in observable stores longer than expected.
  4. HoverService._delayedHovers: Global singleton Map retaining disposed objects via show closure → resolveHoverOptions closure → this. If hover cleanup disposable doesn't fire, the entire object tree is retained.
  5. ObjectMutationLog._previous: The incremental serializer keeps a full snapshot of the last-serialized state. Every loaded ChatModel holds 2x its data: live + _previous.
  6. _previousModelRef pattern: MutableDisposable setter disposes the old value. Reading .value and storing it elsewhere, then setting .value = undefined, disposes the stored reference. Use clearAndLeak() 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 by VSCODE_DEV_DEBUG_OBSERVABLES env var. Retains ALL observed observables. Check if this is active before investigating observable-rooted paths.
  • GCBasedDisposableTracker (FinalizationRegistry): If register(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.

平台分布

Codex

36.48%
按下载量换算83

Claude

28.62%
按下载量换算65

Cursor

16.65%
按下载量换算38

Gemini CLI

9.49%
按下载量换算22

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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