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Run Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs
Install Run Windows E2E
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install DataDog/datadog-agent run-windows-e2e --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/run-windows-e2e .claude/skills/run-windows-e2e && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .claude/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2eType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install DataDog/datadog-agent run-windows-e2e --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/run-windows-e2e .agents/skills/run-windows-e2e && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .agents/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install DataDog/datadog-agent run-windows-e2e --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/run-windows-e2e .cursor/skills/run-windows-e2e && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .cursor/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/DataDog/datadog-agent.git --path .agents/skills/run-windows-e2e--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install DataDog/datadog-agent run-windows-e2e --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/run-windows-e2e .gemini/skills/run-windows-e2e && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .gemini/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install DataDog/datadog-agent run-windows-e2eInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/run-windows-e2e .github/skills/run-windows-e2e && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .github/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add DataDog/datadog-agent --skill run-windows-e2e -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install DataDog/datadog-agent run-windows-e2e --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/DataDog/datadog-agent.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/run-windows-e2e .opencode/skills/run-windows-e2e && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "run-windows-e2e" agent skill from https://github.com/DataDog/datadog-agent/tree/main/.agents/skills/run-windows-e2e into .opencode/skills/run-windows-e2e/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "run-windows-e2e", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
run-windows-e2e- GitHub stars
- 3.8k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 597 words
- Files
- 5 (incl. references)
- Skills in repo
- 35
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs
- Works in 7 steps: Parse $ARGUMENTS → Check prerequisites → Resolve artifact environment variables → …
- Tasks that involve End-to-end testing
- Calls go and pulumi
What it does
Run Windows E2E is an agent skill from DataDog/datadog-agent, published by the product's own GitHub organization. Run Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/running.md`, `references/setup.md` and `references/troubleshooting.md`).
It sits in Testing & QA, covering End-to-end testing. It works with Amazon Web Services. The repository describes itself as: Main repository for Datadog Agent. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve End-to-end testing
Example prompts
- “/run-windows-e2e”
Requirements
- Pre-approved tools (allowed-tools): Bash, Read, Glob, Grep, AskUserQuestion
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 20eff25. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
BashReadGlobGrepAskUserQuestion
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
gopulumi
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Run Windows E2E loads about 1.5k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 32 tokens; SKILL.md has 597 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Bash, Read, Glob, Grep, AskUserQuestion
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from DataDog/datadog-agent at commit 20eff25, republished under its Apache-2.0 licence (© DataDog). 597 words, ~1,459 tokens.
Download SKILL.mdSave it as
.claude/skills/run-windows-e2e/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- run-windows-e2e
- description
- Run Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs
- allowed-tools
- Bash, Read, Glob, Grep, AskUserQuestion
- argument-hint
- [suite] [TestFunctionName] [--build release|pipeline|local] [--pipeline-id <id>] [--version <version>]
Run Windows E2E tests from test/new-e2e/tests/windows/ or test/new-e2e/tests/installer/windows/.
Detailed reference material lives in references/ next to this file — read the
relevant one when a step calls for it rather than duplicating it here:
references/setup.md— prerequisites,~/.test_infra_config.yaml, dev modereferences/running.md—setup-env, local builds,go testflagsreferences/vm-access.md— connecting to a dev-mode VM (RDP/SSH)references/troubleshooting.md— test outputs, Pulumi locks, AWS profile
Instructions
Step 1 — Parse $ARGUMENTS
Determine:
- Suite: which test suite to run (e.g.
install-test,service-test,agent-package,install-script). If not provided, ask the user. - Test function: specific
TestXxxfunction. Most suites expect exactly one test per run — ask the user which one if not specified. - Artifact source:
--build pipeline(default),--build local, or--build release; pass--pipeline-id <id>through if given. - Stable/previous version (upgrade tests only): if the user specifies a version to upgrade from, plan a second
setup-envrun with--prefix STABLE_AGENTin Step 3 (seereferences/running.md"Upgrade tests"). - Branch: if the user mentions a branch ("from main"), pass
--branch <name>tosetup-env. The default is the current git branch, which may have no pipelines if it's a local feature branch.
Map suite names to Go package paths:
| Suite | Package path |
|---|---|
install-test | ./test/new-e2e/tests/windows/install-test |
service-test | ./test/new-e2e/tests/windows/service-test |
fips-test | ./test/new-e2e/tests/windows/fips-test |
domain-test | ./test/new-e2e/tests/windows/domain-test |
| installer / Fleet Automation (agent-package, install-script, install-exe, ddot, apm-inject, …) | ./test/new-e2e/tests/installer/windows |
The installer / Fleet Automation tests are all one flat package (the
suites/<package>/ subdirectories were flattened in #47161) — pick the area by
test function with -run (e.g. TestAgentUpgrades, TestInstallScript,
TestDDOTExtensionViaMSI, TestAPMInjectInstalls).
If the user gives a partial name or test function, search with Glob/Grep under test/new-e2e/tests/windows/ and test/new-e2e/tests/installer/windows/ to resolve it.
Step 2 — Check prerequisites
test -f ~/.test_infra_config.yaml && echo "EXISTS" || echo "MISSING"
pulumi version 2>/dev/null || echo "MISSING"If either is missing, offer to run dda inv e2e.setup and wait for the user to
complete its interactive prompts. If prerequisites exist but devMode is not
set, mention that devMode: true reuses VMs across runs (much faster for
iterative development). Full detail in references/setup.md.
Step 3 — Resolve artifact environment variables
Run setup-env with --fmt json to capture the required env vars (no shell
eval needed — prepend the parsed pairs inline to go test in Step 5).
# From a pipeline (most common)
dda inv new-e2e-tests.setup-env --build pipeline --fmt json [--branch <branch>] [--pipeline-id <id>]
# From a local build (run `dda inv msi.build` first, + `msi.package-oci` for installer/OCI tests)
dda inv new-e2e-tests.setup-env --build local --fmt jsonFor upgrade tests, run a second time with --prefix STABLE_AGENT and merge the
vars in. GitLab token handling, local-build details, and the STABLE_AGENT
flow are in references/running.md.
Show full SKILL.md (232 more words)Show less
Step 4 — Check for stale state (dev mode only)
If devMode: true and the user is rerunning, the previous VM may still have the
agent installed. Ask whether they've cleaned up (MSI tests: uninstall the agent;
installer tests: datadog-installer.exe purge). See
references/running.md "Clean state between runs".
Step 5 — Build and confirm the go test command
go test -v -timeout 30m -tags test <package-path> -run <TestFunction>$Two rules to apply (rationale in references/running.md):
anchor the -run regex with $ at both suite and subtest level, and use the
exact package path with no trailing /... (or output won't stream).
Show the full command to the user and confirm before running.
Step 6 — Run the test
Warn the user that AWS SSO auth may open a browser window when the test starts
(the test pauses until login completes), and that a non-sandbox AWS_PROFILE
will cause auth errors — advise unset AWS_PROFILE.
Run with run_in_background: true since tests provision real AWS VMs.
Provisioning takes a few minutes; once the VM is up, SSH becomes available
within ~60s (Linux) / ~180s (Windows). If SSH is not available within those
windows, troubleshoot before assuming the test is still running normally
(see references/vm-access.md).
Step 7 — Report results
When the test completes:
- Report pass/fail.
- On failure, point the user to
~/e2e-output/latest/(crash dumps, agent/installer logs, event logs). IfdevModeis on, the VM is still up — offer to help RDP/SSH in viareferences/vm-access.md. - For Pulumi lock errors or AWS auth errors, see
references/troubleshooting.md.
© DataDog, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (references) in .agents/skills/run-windows-e2e of DataDog/datadog-agent.
- SKILL.md
- references/running.md
- references/setup.md
- references/troubleshooting.md
- references/vm-access.md
Open the folder on GitHubat commit 20eff25
Compare with similar skills
Run Windows E2E next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Run Windows E2E this skillDataDog/datadog-agent | 3.8k | — | ~1.5k | Automated safety check: Notes | Apache-2.0 | |
| Crabboxopenclaw/openclaw | 392k | — | ~3.7k | Automated safety check: Pass | MIT | |
| New Integgo-to-k/cdkd | 143 | — | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Web Application Testinganthropics/skills | 180k | 51 repos | ~966 | Automated safety check: Pass | Apache-2.0 | |
| OpenHarness End-to-End EvalsHKUDS/OpenHarness | 16k | 1 repos | ~2.1k | Automated safety check: Notes | MIT | |
| playwright-cli Browser Automationgithub/gh-aw | 5.4k | 24 repos | ~2.8k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Run Windows E2E
What does Run Windows E2E do?
Run Windows E2E tests (MSI install tests or Fleet Automation/installer tests) locally against AWS-provisioned VMs. Run Windows E2E is an agent skill from DataDog/datadog-agent, published by the product's own GitHub organization.
When should I use Run Windows E2E?
Run Windows E2E fits situations like: tasks that involve End-to-end testing.
How do I install Run Windows E2E in Claude Code?
Run `npx skills add DataDog/datadog-agent --skill run-windows-e2e -a claude-code`. Or copy the skill folder (.agents/skills/run-windows-e2e in DataDog/datadog-agent) into .claude/skills/run-windows-e2e in your project. Claude Code loads it when a task matches its description.
How do I install Run Windows E2E in Codex?
Run `npx skills add DataDog/datadog-agent --skill run-windows-e2e -a codex`. Or copy the skill folder (.agents/skills/run-windows-e2e in DataDog/datadog-agent) into .agents/skills/run-windows-e2e in your project. Codex loads it when a task matches its description.
Can I use Run Windows E2E in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DataDog/datadog-agent --skill run-windows-e2e -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/run-windows-e2e, .gemini/skills/run-windows-e2e, .github/skills/run-windows-e2e and .opencode/skills/run-windows-e2e in your project.
What does Run Windows E2E need to run?
Going by SKILL.md and its folder, Run Windows E2E needs the command-line tools its instructions call (go and pulumi). Its frontmatter pre-approves these tools: Bash, Read, Glob, Grep, AskUserQuestion.
Does Run Windows E2E access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Run Windows E2E safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.
What licence does Run Windows E2E use?
Run Windows E2E is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Run Windows E2E use?
About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Run Windows E2E?
Skills that share tags, products or a category with Run Windows E2E: Crabbox (openclaw/openclaw, 392k stars), New Integ (go-to-k/cdkd, 143 stars), Web Application Testing (anthropics/skills, 180k stars) and OpenHarness End-to-End Evals (HKUDS/OpenHarness, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Run Windows E2E?
DataDog (a GitHub organization, an official publisher) maintains it in DataDog/datadog-agent, which has 3,757 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 8, 2026.
Source: DataDog/datadog-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.