Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/microsoft/vscode/auto-perf-optimizenpx skills add microsoft/vscode --skill auto-perf-optimizegit clone --depth 1 https://github.com/microsoft/vscodeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00062 | $0.04551 |
| Opus 5 | $0.00031 | $0.02276 |
| Sonnet 5 | $0.00012 | $0.00910 |
| Haiku 4.5 | $0.00006 | $0.00455 |
Grade A, and why
auto-perf-optimize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VS Code Performance Workflow
Drive a repeatable VS Code scenario, collect memory/performance artifacts, verify that the scenario actually happened, then hand the resulting heap snapshots to the generic heap-snapshot-analysis skill when object-level investigation is needed.
When to Use
- User describes a VS Code workflow and asks whether it leaks or grows memory
- User asks the agent to launch VS Code, drive a scenario, and capture heap snapshots
- User asks to run the Chat memory smoke runner bundled with this skill
- User wants screenshots,
summary.json, renderer heap samples, and targeted.heapsnapshotfiles for one scenario - User wants a new automation runner for a non-Chat VS Code scenario
Do not use this skill when snapshots already exist and the user only wants heap object/retainer analysis. Use heap-snapshot-analysis directly.
The Story
- Define the scenario. Write down one warmup action, one repeatable iteration, and one quiescent point where it is fair to force GC and sample memory.
- Develop the automation. Start with a tiny no-snapshot run. If it fails or the UI state is uncertain, keep the Code window open, connect
@playwright/clito the same CDP port, take workspace-local screenshots, inspect snapshots, and update the runner's selectors/waits. - Run a fast smoke. Disable heap snapshots first. Prove the scenario completes and the artifact summary says what you think it says.
- Capture targeted snapshots. Snapshot a warmed-up baseline and a later iteration. Do not snapshot every sample unless necessary; snapshots are huge and slow.
- Verify the run. Inspect
summary.jsonand screenshots. Do not analyze a failed login, trust prompt, stuck progress row, or wrong UI state. - Analyze snapshots. Switch to heap-snapshot-analysis for compare scripts, object grouping, and retainer paths.
- Fix and verify. After identifying leaks, make product-code fixes. Then rerun the same scenario with the same snapshot labels and compare like-for-like. Do not stop at analysis — the goal is to ship a fix, not just a report.
- Document. Save a summary of findings, fixes, and before/after measurements to session memory so the work is preserved.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 279 lines · 62 tokens per session scan A e15462593f90
auto-perf-optimize is a skill published in the GitHub repository microsoft/vscode (190,384 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 4,551 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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cyrene-plan-mode
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as-spec-driven-development
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ecc-code-tour
Create CodeTour .tour files — persona-targeted, step-by-step walkthroughs with real file and line anchors. Use for onboarding tours, architecture walkthroughs, PR tours, RCA tours, and structured "explain how this works" requests.
ecc-codebase-onboarding
Analyze an unfamiliar codebase and generate a structured onboarding guide with architecture map, key entry points, conventions, and a starter CLAUDE.md. Use when joining a new project or setting up Claude Code for the first time in a repo.