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 hermes-agentgit 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/hermes-agent)<a href="https://agentmods.dev/skills/amirkiarafiei/subagent-cli-skills/hermes-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/subagent-cli-skills/hermes-agent/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/hermes-agent"><img src="https://agentmods.dev/badge/skills/amirkiarafiei/subagent-cli-skills/hermes-agent.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 | $0.00081 | $0.02519 |
| Opus 5 | $0.00041 | $0.01260 |
| Sonnet 5 | $0.00016 | $0.00504 |
| Haiku 4.5 | $0.00008 | $0.00252 |
Grade A, and why
hermes-agent 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 10d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes Agent CLI (subagent/task delegation)
Use Hermes Agent CLI to run a separate long-horizon pass over the repo: multi-step implementation, broad refactors, batch file writes, deep research, or code review—similar to handing a task to a subagent. You stay orchestrator: smaller prompts, less context burn, often lower spend than doing the same work entirely in-session.
Hermes Agent provides a rich tool system with file editing (patch, read_file, write_file, search_files), terminal access, web search/extraction, browser automation, delegation (delegate_task), code execution, and a large built-in skills catalog. It supports multiple model providers (Nous Portal, OpenRouter, Anthropic, OpenAI, Ollama, etc.) and can run background tasks in parallel.
When to use Hermes Agent CLI
- Large or multi-step work: several files, phases, or checkpoints (feature slice, migration, test suite, docs sweep).
- Deep research: Built-in web search, web extraction, and browser automation for documentation digging or investigative research.
- Code review: Built-in
requesting-code-reviewskill andgithub-code-reviewskill for PR-style analysis. - Heavy code generation or editing: Hermes drives tool use (file tools, terminal) while you summarize outcomes and merge.
- Parallel work: Use
/backgroundordelegate_taskto spawn isolated subagent sessions for concurrent tasks. - Background investigations: Start long-running research tasks while you continue working in the foreground.
- Plan-then-execute: Use the built-in
planskill for structured markdown planning before execution. - User explicitly asks for Hermes or "use Hermes Agent for this."
When not to use
- Small / single-step tasks answerable with one or two edits or a short explanation.
- Tight feedback loops where the user wants rapid back-and-forth refinement in one thread.
- Secrets or policy-sensitive flows—avoid piping credentials; redact before delegating.
- Already-loaded context where duplicating the whole plan adds no value—handle locally.
- Low ROI (Return on Investment): If the task is "needle-in-a-haystack" (requires high precision over a single line) or if the time to compose the Handoff Table exceeds the time to simply edit the file locally. Delegation should only be used when the "mental offloading" outweighs the "handoff overhead."
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 139 lines · 81 tokens per session scan A 7e4953b1b239
hermes-agent is a skill published in the GitHub repository amirkiarafiei/subagent-cli-skills (5 stars, last pushed 3d ago), licensed MIT. It adds 81 tokens to every session and 2,519 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
review
Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.
work
Execute an approved wish plan — orchestrate subagents per task group with fix loops, validation, and review handoff.
brainstorm
Explore ambiguous or early-stage ideas interactively — tracks wish-readiness and crystallizes into a design for wish.
wish
Convert an idea into a structured wish plan with scope, acceptance criteria, and execution groups for work.
report
Investigate bugs comprehensively — cascade through trace, capture browser evidence, extract observability data, and prepare or explicitly create a GitHub issue with grounded findings.
project-context
Use PowerContext project memory and handoff tools through MCP when continuing prior work, recalling decisions, maintaining durable memory, or transferring work across tasks, sessions, or agents.