Visual Studio Code is a code editor that supports editing, navigating, understanding, debugging, and extending software projects. Developers use it for the edit-build-debug cycle, and the catalogue add-ons provide skills, instructions, and agents for working within the editor.
Borrowing it
Nothing to install: this file belongs to microsoft/vscode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/vscode/main/.github/skills/ci-artifact-testing/SKILL.mdgit clone --depth 1 https://github.com/microsoft/vscodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/vscode/ci-artifact-testing)<a href="https://agentmods.dev/skills/microsoft/vscode/ci-artifact-testing"><img src="https://agentmods.dev/badge/skills/microsoft/vscode/ci-artifact-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/microsoft/vscode/ci-artifact-testing"><img src="https://agentmods.dev/badge/skills/microsoft/vscode/ci-artifact-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00061 | $0.01609 |
| Opus 5.5 | $0.00024 | $0.00644 |
| Sonnet 5.5 | $0.00012 | $0.00322 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
ci-artifact-testing 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 13d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test with CI artifacts
Prefer an existing compatible CI build when checking whether an upstream change fixes a downstream regression. A successful upstream workflow is not proof that the VS Code scenario passes: run the downstream test with the candidate artifact.
This workflow applies to runtime executables, native libraries, SDK packages, and packaged applications. Use the relevant integration-test, unit-test, or smoke-test workflow for execution. For Azure-hosted artifacts, use azure-pipelines for discovery and retrieval; the provenance and comparison rules below still apply.
1. Identify the candidate before building
- Record the upstream repository, PR number, exact head SHA, and intended regression. Record the downstream checkout revision and bundled dependency version.
- Check CI artifacts before cloning, installing dependencies, or starting a local build. Inspect the producer workflow when artifact names or packaging are unclear.
- Execute only artifacts from an authorized source. Treat PR binaries as code execution, use isolated test state, and do not supply live credentials to a deterministic replay test.
GitHub Actions discovery example (replace the illustrative values):
repo=owner/repository
pr=123
gh pr view "$pr" --repo "$repo" \
--json url,state,headRefOid,baseRefOid
head_sha=$(gh pr view "$pr" --repo "$repo" \
--json headRefOid --jq .headRefOid)
gh api --paginate \
"repos/$repo/actions/runs?head_sha=$head_sha&per_page=100" \
--jq '.workflow_runs[] | {id,name,event,status,conclusion,head_sha,html_url}'
Select a run that produced the relevant build, not just a lint or test-report artifact:
run_id=123456
gh api --paginate \
"repos/$repo/actions/runs/$run_id/artifacts?per_page=100" \
--jq '.artifacts[] | {id,name,size_in_bytes,expired,digest,workflow_run}'
List artifacts for the selected run rather than relying on the repository-wide latest-artifacts page. Busy repositories can push the desired artifact off that page. For reusable or downstream workflows, follow the producer relationship and verify the source revision instead of assuming its run uses the PR head.
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.
- 13d ago First seen · 97 lines · 61 tokens per session scan A 82dd9e3110e9
ci-artifact-testing is a skill published in the GitHub repository microsoft/vscode (193,657 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 1,609 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-03.
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