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 skills add xmake-io/xmake-skills --skill xmake-async-jobsgit clone --depth 1 https://github.com/xmake-io/xmake-skillsWrote 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/xmake-io/xmake-skills/xmake-async-jobs)<a href="https://agentmods.dev/skills/xmake-io/xmake-skills/xmake-async-jobs"><img src="https://agentmods.dev/badge/skills/xmake-io/xmake-skills/xmake-async-jobs/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/xmake-io/xmake-skills/xmake-async-jobs"><img src="https://agentmods.dev/badge/skills/xmake-io/xmake-skills/xmake-async-jobs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00097 | $0.02578 |
| Opus 5 | $0.00048 | $0.01289 |
| Sonnet 5 | $0.00019 | $0.00516 |
| Haiku 4.5 | $0.00010 | $0.00258 |
Grade A, and why
xmake-async-jobs scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
os.vrunv("curl", {"-O", urls[i]}) How it starts
The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Async Jobs, Parallelism & Scheduling
Xmake's Lua layer has a cooperative coroutine scheduler underneath. Any on_build/on_build_files/on_install hook you write runs inside it, and can spawn parallel subjobs via async.runjobs (with three different input shapes) or by yielding explicitly via the scheduler.
Use this skill when writing a rule or task that needs to do work in parallel — file generation, batch downloads, distributed jobs, etc.
1. async.runjobs — the main entry point
import("async.runjobs")
runjobs("my-jobs", function (index, total, opt)
print("job %d/%d", index, total)
os.sleep(500)
end, {
total = 100,
comax = 6
})
Parameters:
name— used in scheduler output / timing.jobs— can be a function (calledtotaltimes), ajobpool, or ajobgraph.- Options:
total— required whenjobsis a function.comax— max concurrent coroutines (default 4).timeout— ms foron_timercallback (default 500).on_timer = function(indices)— periodic callback with running job indices.on_exit = function(abort_errors)— called after all jobs finish or error.waiting_indicator = true— simple spinner; or{chars = {'/', '-', '\\', '|'}}.curdir— set cwd for each job.isolate = true— isolate coroutine environments.distcc = client— farm out to a distcc client.remote_only = true— force remote execution.
Example with timer:
runjobs("download", function (i, total, opt)
os.vrunv("curl", {"-O", urls[i]})
end, {
total = #urls,
comax = 8,
timeout = 1000,
on_timer = function (indices)
utils.vprint("still running: %s", table.concat(indices, ","))
end
})
2. async.jobgraph — DAG-ordered jobs
When jobs have dependencies (A must finish before B), build a job graph:
import("async.jobgraph")
import("async.runjobs")
local jobs = jobgraph.new()
local function jobfunc(i, total, opt)
print("job %s (%d/%d)", opt.progress:percent(), i, total)
end
jobs:add("root", jobfunc)
for i = 1, 3 do
jobs:add("mid/" .. i, jobfunc)
jobs:add_orders("mid/" .. i, "root") -- mid/i depends on root
for j = 1, 5 do
local leaf = "leaf/" .. i .. "/" .. j
jobs:add(leaf, jobfunc)
jobs:add_orders(leaf, "mid/" .. i)
end
end
runjobs("build", jobs, {comax = 4})
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.
- 12d ago First seen · 287 lines · 97 tokens per session scan A 6600f1a44cdf
xmake-async-jobs is a skill published in the GitHub repository xmake-io/xmake-skills (24 stars, last pushed 19d ago), licensed Apache-2.0. It adds 97 tokens to every session and 2,578 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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