X-CMD is a POSIX-shell toolkit that provides reusable shell modules and on-demand command-line packages for bash, zsh, ash, and dash. It is designed for people and AI agents that need shell utilities, integrations, and agent-related workflows, and the catalogue contains a large collection of skills for using it.
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 x-cmd/x-cmd --skill loopgit clone --depth 1 https://github.com/x-cmd/x-cmdWrote 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/x-cmd/x-cmd/loop)<a href="https://agentmods.dev/skills/x-cmd/x-cmd/loop"><img src="https://agentmods.dev/badge/skills/x-cmd/x-cmd/loop/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/x-cmd/x-cmd/loop"><img src="https://agentmods.dev/badge/skills/x-cmd/x-cmd/loop.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 3 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 | $0.00025 | $0.00394 |
| Opus 5.5 | $0.00010 | $0.00158 |
| Sonnet 5.5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Loop Runner
Trigger
Use when:
- User says
/loop <interval> <prompt> - User wants recurring monitoring or periodic execution
Steps
Step 1: Parse Arguments
/loop <interval> <prompt>
Interval patterns:
| Pattern | Cron Expression |
|---|---|
Nm (N ≤ 59) |
*/N * * * * |
Nh (N ≤ 23) |
0 */N * * * |
Nd |
0 0 */N * * |
Round non-dividing intervals to nearest clean value.
Step 2: Clarify Intent
Ask user:
- What to monitor (parse prompt)
- How to execute (which x-cmd module or command)
- Where to store results
Step 3: Configure Notification
Ask before creating:
- Memory (default) — store for later review
- Mailbox — notification on new results
- Other user-specified method
Step 4: Create Cron Job
x cron add \
--name "loop-$(date +%Y%m%d%H%M%S)" \
--desc "<description>" \
"<cron-expression>" \
"<full-command>"
Step 5: Execute Immediately
Run the command now; do not wait for first cron fire.
Step 6: Confirm
Report to user:
- Job name and cron expression
- Human-readable cadence
- Monitoring target
- Result storage location
- Cancellation:
x cron rm <name>
Constraints
- Always ask about result storage before creating
- Always execute immediately
- Round messy intervals and inform user
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.
- 1mo ago First seen · 74 lines · 25 tokens per session scan A eac6df3133f2
loop is a skill published in the GitHub repository x-cmd/x-cmd (4,717 stars, last pushed 7d ago), licensed Apache-2.0. It adds 25 tokens to every session and 394 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
completion-script-generator
Generates shell completion scripts for CLI tools. Parses command structure from help output, commander/yargs configs, or manual specification. Produces completions for bash, zsh, and fish that follow each shell's conventions.
dnanexus-integration
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
latchbio-integration
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
lambda-gpu-cloud
Safely inspect and operate Lambda Cloud GPU instances through the documented Cloud API and SSH, with explicit approval before billable or destructive actions.
modal-serverless-gpu
Run approved CPU or GPU work through OpenScience computejob on the user's configured Modal account. Use for isolated scientific scripts, dependency provisioning, durable outputs, logs, status, cancellation, and recovery. Never invoke the Modal SDK or CLI directly.
tensorpool-gpu-cloud
Safely inspect and operate TensorPool GPU clusters and jobs using the current tp CLI, with explicit approval before any billable or destructive action.