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 nimadorostkar/Claude-Skills-collection --skill subagentsgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/subagents)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/subagents"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/subagents/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/nimadorostkar/claude-skills-collection/subagents"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/subagents.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 | $0.00044 | $0.01238 |
| Opus 5 | $0.00022 | $0.00619 |
| Sonnet 5 | $0.00009 | $0.00248 |
| Haiku 4.5 | $0.00004 | $0.00124 |
Grade A, and why
subagents 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 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.
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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagents
Purpose
Delegate work to a subagent when its own context is worth the cost of starting one. A subagent begins cold — it re-derives everything the main agent already knows — so the delegation must buy more than it costs.
When to Use
- A search or exploration that would flood the main context with results you do not need to keep.
- Independent work that can run in parallel.
- A task requiring a different, specialized instruction set.
- Verification by an agent that has not seen the reasoning that produced the work.
Capabilities
- Scoping a subagent's task and boundaries.
- Prompt design for a cold-start agent.
- Parallel fan-out for independent work.
- Result aggregation.
- Read-only agents for review and verification.
Inputs
- The task, and whether it is genuinely independent.
- What the subagent needs to know, since it knows nothing.
- What it must return.
Outputs
- A subagent that completes its task without further interaction.
- A result compact enough to be worth the round trip.
Workflow
- Justify the delegation — The valid reasons are: context isolation (the search produces 50 files of noise and one answer), parallelism (five independent tasks), and specialization (a different instruction set). "The task is big" is not a reason — a big task in the same context is usually cheaper.
- Write the prompt as if to a stranger — Because it is one. The subagent has none of your context: no conversation history, no prior findings, no shared understanding of the goal. Everything it needs must be in the prompt.
- State exactly what to return — A subagent that returns a wall of text has moved the context problem rather than solved it. Specify the format and the length.
- Fan out only genuinely independent work — Two subagents editing the same file will conflict. Parallelism requires disjoint scopes.
- Use a read-only agent for verification — An agent that did not write the code is a better reviewer of it than the one that did.
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 · 115 lines · 44 tokens per session scan A 1e0f7c0b754c
subagents is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 44 tokens to every session and 1,238 once invoked, about $0.0002 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.
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