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 AtlasOmnia/hermes-custom-pack --skill hermes-self-evaluationgit clone --depth 1 https://github.com/AtlasOmnia/hermes-custom-packWrote 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/atlasomnia/hermes-custom-pack/hermes-self-evaluation)<a href="https://agentmods.dev/skills/atlasomnia/hermes-custom-pack/hermes-self-evaluation"><img src="https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/hermes-self-evaluation/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/atlasomnia/hermes-custom-pack/hermes-self-evaluation"><img src="https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/hermes-self-evaluation.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 | $0.00069 | $0.02567 |
| Opus 5.5 | $0.00028 | $0.01027 |
| Sonnet 5.5 | $0.00014 | $0.00513 |
| Haiku 4.5 | $0.00007 | $0.00257 |
Grade A, and why
hermes-self-evaluation 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.
This is a copy
100% identical to hermes-self-evaluation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes Self-Evaluation
Use this skill when the user asks to audit, review, or optimize Hermes's own performance — analyzing session data, skills, configuration, costs, and usage patterns to identify improvements, automation opportunities, and system optimizations.
Don't use for: skill content quality grading (use skill-auditor instead), one-off task analysis, or session artifact indexing after a work session (use session-artifact-indexing).
Overview
The core workflow: gather live evidence about Hermes's state and history → validate each finding against the subsystem's actual semantics and control surface → either produce a direct evidence-backed review or compose a structured analyst prompt for an independent model → verify recommendations before implementation.
External models are useful anomaly detectors and critics, but they are not automatically reliable root-cause analysts. Separate the observed symptom, supported interpretation, confirmed producer/root cause, and proposed change. Before acting on any self-check finding,
When to Use
Triggers:
- "How can we improve Hermes?"
- "Analyze my sessions and tell me what to optimize"
- "I want to have another model evaluate X"
- "Where do sessions/skills live so I can analyze them?"
- "Do an audit of the system"
- "What's the token cost breakdown?"
Workflow
Fast Path: Evaluate a Single Runaway Session
When the user names a specific session and says it was “working” too long, hit a tool-call guardrail, ignored “stop,” or needs the problem evaluated, do not build a broad system-audit prompt first. Diagnose the named session directly.
Use for the exact SQL/Python checks. Minimum evidence to collect:
- Session metadata from
~/.hermes/state.db: source, title, model, start/end times, message count, tool-call count, token totals, end reason. - Role counts and top tool counts.
- User-message timeline and non-tool assistant replies, especially around compaction/restore and the latest steering instruction.
- Repeated assistant
tool_callIDs. Exact repeatedcall_ids are a strong sign of stale tool-call replay after context compaction or gateway restore. - Log markers for the session ID:
max_iterations_reached,Preflight compression,Pre-API compression,gateway shutdown,Operation interrupted,tool-call guardrail,idempotent_no_progress, and transport retry loops. - If relevant, check whether any live process from the runaway task is still active before saying it is safe to abandon.
What ships with it
3 files 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.
- 1mo ago First seen · 208 lines · 69 tokens per session scan A f3eb1f6600da
hermes-self-evaluation is a skill published in the GitHub repository AtlasOmnia/hermes-custom-pack (72 stars, last pushed 13d ago), licensed MIT. It adds 69 tokens to every session and 2,567 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to hermes-self-evaluation, differing in 0 lines, and is treated as a copy.
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