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 tkellogg/open-strix --skill long-running-jobsgit clone --depth 1 https://github.com/tkellogg/open-strixWrote 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/tkellogg/open-strix/long-running-jobs)<a href="https://agentmods.dev/skills/tkellogg/open-strix/long-running-jobs"><img src="https://agentmods.dev/badge/skills/tkellogg/open-strix/long-running-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/tkellogg/open-strix/long-running-jobs"><img src="https://agentmods.dev/badge/skills/tkellogg/open-strix/long-running-jobs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 165 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00068 | $0.02153 |
| Opus 5 | $0.00034 | $0.01077 |
| Sonnet 5 | $0.00014 | $0.00431 |
| Haiku 4.5 | $0.00007 | $0.00215 |
Grade A, and why
long-running-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 11d 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.
shell(command="wget -q -O /data/model.bin https://example.com/big-model.bin", async_mode=True) How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long-Running Jobs
Some commands take minutes or hours. Rather than blocking on them, spawn them with async_mode=True. The harness owns the process — captures stdout/stderr to files, surfaces the job in the web UI, and fires a completion event back into the agent's queue when the subprocess exits.
The completion event is a fresh turn: journal, memory blocks, and recent messages get rebuilt from disk. There is no in-context state to preserve. Whatever you need to remember when the job finishes has to live somewhere durable (journal entry, state file, the command itself) before you spawn.
When to Use This
USE when:
- Build commands (
cargo build,npm run build,make) - Test suites that take more than ~30 seconds
- Data processing, model training, large downloads
- Deployments or migrations
- Sub-agent invocations (
acpx,codex exec, etc.) - Any command where you want to keep working while it runs
DON'T USE when:
- Quick commands (< 30s) — just call shell normally and wait
- Commands you need the result of immediately (chain into next reasoning step)
- Interactive commands that need stdin
The Canonical Pattern
shell(command="cargo build --release", async_mode=True)
That's it. The harness:
- Spawns the command in its own process group, file-backs stdout/stderr
- Returns immediately with
Spawned async job j_abc123 (pid 12345) - Surfaces the job in the web UI with a live status indicator
- When the subprocess exits, fires a
shell_job_completeevent that wakes the agent with a prompt containingjob_id,command,exit_code,elapsed, and the tail (~4KB) of each stream
You keep working in the current turn. The completion event arrives as a separate wake-up.
Inspecting Running Jobs
While a job is running you have three options — and one of them isn't a tool call:
- Web UI — the user can see running jobs at a glance with elapsed time and status. If the user's watching, they already know.
shell_jobs_list()— every job the registry knows about (running + recently finished), withjob_id,pid,status,elapsed_seconds, andseconds_since_last_signal.shell_job_output(job_id, tail_lines=N, stream="stdout"|"stderr"|"both")— tail current output without waiting for completion. Useful for sanity-checking that a build is making progress vs hung.
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.
- 11d ago First seen · 184 lines · 68 tokens per session scan A 235a3f735879
long-running-jobs is a skill published in the GitHub repository tkellogg/open-strix (85 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 2,153 once invoked, about $0.0003 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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