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/donna-starter --skill hermes-self-evaluationgit clone --depth 1 https://github.com/AtlasOmnia/donna-starterWrote 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/donna-starter/hermes-self-evaluation)<a href="https://agentmods.dev/skills/atlasomnia/donna-starter/hermes-self-evaluation"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/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/donna-starter/hermes-self-evaluation"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/hermes-self-evaluation.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 76 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 | $0.00069 | $0.02567 |
| Opus 5 | $0.00034 | $0.01283 |
| Sonnet 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- hermes-self-evaluation — 100% identical, 0 lines differ
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.
- 10d ago First seen · 208 lines · 69 tokens per session scan A f3eb1f6600da
hermes-self-evaluation is a skill published in the GitHub repository AtlasOmnia/donna-starter (107 stars, last pushed 10d ago), licensed MIT. It adds 69 tokens to every session and 2,567 once invoked, about $0.0003 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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