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/agent0ai/space-agent/usersnpx skills add agent0ai/space-agent --skill usersgit clone --depth 1 https://github.com/agent0ai/space-agentWhat 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 | $0.00028 | $0.00959 |
| Opus 5 | $0.00014 | $0.00479 |
| Sonnet 5 | $0.00006 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
Admin Users 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Admin Users — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill for concrete user-account work.
First Check
- Call
const info = await space.api.userSelfInfo(). - Confirm
info.groups.includes("_admin") === true. - Prefer logical app paths derived from the standard layer rules, not guessed disk paths.
Canonical User Tree
L2/<username>/is the user's logical root.L2/<username>/user.yamlstores user metadata such asfull_name.L2/<username>/meta/password.jsonstores the backend-sealed SCRAM verifier.L2/<username>/meta/logins.jsonstores signed session verifiers.L2/<username>/mod/is that user's customware module root.
There is no separate user registry file. The watched user index is derived from files under L2/<username>/.
Path Rules
- Use logical paths such as
L2/alice/user.yaml. - When writable storage is relocated under
CUSTOMWARE_PATH, these logical paths stay the same. fileWrite(".../")creates a directory because the path ends with/.- Do not hand-craft
meta/password.jsonor individual session entries. Usepassword_generatefor password records and only write{}when revoking sessions.
Create A User
Use a normalized username segment such as alice, ops_bot, or qa-team-1.
const username = "alice";
const fullName = "Alice Example";
const password = "replace-me";
const verifier = await space.api.call("password_generate", {
method: "POST",
body: { password }
});
return await space.api.fileWrite({
files: [
{ path: `L2/${username}/` },
{ path: `L2/${username}/mod/` },
{
path: `L2/${username}/user.yaml`,
content: space.utils.yaml.stringify({ full_name: fullName })
},
{
path: `L2/${username}/meta/password.json`,
content: `${JSON.stringify(verifier, null, 2)}\n`
},
{
path: `L2/${username}/meta/logins.json`,
content: "{}\n"
}
]
});
Update User Metadata
Read user.yaml, parse it, mutate the fields you need, and write it back.
const path = "L2/alice/user.yaml";
const current = await space.api.fileRead(path);
const config = space.utils.yaml.parse(current.content || "");
config.full_name = "Alice Example";
return await space.api.fileWrite(path, space.utils.yaml.stringify(config));
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 · 130 lines · 28 tokens per session scan A 966df9494a2a
Admin Users is a skill published in the GitHub repository agent0ai/space-agent (1,390 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 959 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.