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 amirkiarafiei/subagent-cli-skills --skill _skill_templategit clone --depth 1 https://github.com/amirkiarafiei/subagent-cli-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/amirkiarafiei/subagent-cli-skills/_skill_template)<a href="https://agentmods.dev/skills/amirkiarafiei/subagent-cli-skills/_skill_template"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/subagent-cli-skills/_skill_template/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/amirkiarafiei/subagent-cli-skills/_skill_template"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/subagent-cli-skills/_skill_template.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00073 | $0.01872 |
| Opus 5.5 | $0.00029 | $0.00749 |
| Sonnet 5.5 | $0.00015 | $0.00374 |
| Haiku 4.5 | $0.00007 | $0.00187 |
Grade A, and why
<skill-name> 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 25d 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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
(subagent / task delegation)
What is it
This skill teaches your agent to use an agent from another vendor as a subagent.
Almost every coding agent has a headless mode. Headless mode runs the agent from the terminal with one command and returns the answer to stdout. This skill gives the command pattern for one such agent, and the protocol to transfer enough context with the task.
You stay the main agent. You keep the plan, the decisions and the conversation with the user. The subagent does one bounded task and reports back.
When to delegate
- The user asks for it. The user names another agent, or asks for a second opinion.
- Large or multi-step work. The task covers several files, phases or checkpoints.
- Code review with a fresh mind. Another agent reviews the work with a different lens and no memory of the decisions that produced it.
- Planning that needs different ideas. A second agent proposes options that you did not consider.
- An internal council. Several subagents answer the same question, and you compare the answers.
- Deep research. The other CLI has web search, web extraction or browser automation.
- Background investigation. A long task runs in the background while you continue in the foreground.
When not to delegate
- Small or single-step tasks. One or two edits, or a short explanation, are faster in this thread.
- Tight feedback loops. The user wants quick back-and-forth in one conversation.
- Secrets or policy-sensitive work. Do not pipe credentials into a subagent. Redact first.
- Context you already hold. Repeating the whole plan in a prompt adds no value.
- Low return on investment. If writing the handoff costs more than doing the edit, do the edit.
- Work that needs high precision and full judgement. A subagent without your context will break a task that depends on a detail only this session knows.
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.
- 25d ago Changed · +11 lines 3bba1f4b9b1d
- 26d ago First seen · 150 lines · 73 tokens per session scan A 0161f2d9d565
<skill-name> is a skill published in the GitHub repository amirkiarafiei/subagent-cli-skills (12 stars, last pushed 24d ago), licensed MIT. It adds 73 tokens to every session and 1,872 once invoked, about $0.0003 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 2026-09-20.
Other skills, from other repositories
wish
Deliver one decided task end to end — admit it, work it in one worktree, gate, independent review, bounded repair, a merge-ready PR — or plan a multi-group wish when it is bigger than one task.
work
Execute an approved wish in dependency order with scoped workers, independent review, bounded repairs, and verified completion.
brainstorm
Explore an ambiguous idea with the user, settle scope and success criteria, and produce an independently reviewed design for wish.
report
Investigate a failure to its root cause with grounded evidence, hand the diagnosis to fix, and create a GitHub issue only when asked.
static
Stash is your personal home for agent work. Everything you create is scoped to your account — it is yours alone, with nothing to pick or set up first. Your Stash has three primary surfaces.
council
Assess a proposal through independent technical, product, risk, and dissenting lenses, then synthesize a decision without mutating unless explicitly requested.