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 Gesondian/ai-collab-governance-skills --skill hermes-loop-engineeringgit clone --depth 1 https://github.com/Gesondian/ai-collab-governance-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/gesondian/ai-collab-governance-skills/hermes-loop-engineering)<a href="https://agentmods.dev/skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering"><img src="https://agentmods.dev/badge/skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering/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/gesondian/ai-collab-governance-skills/hermes-loop-engineering"><img src="https://agentmods.dev/badge/skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering.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 | $0.00032 | $0.00756 |
| Opus 5 | $0.00016 | $0.00378 |
| Sonnet 5 | $0.00006 | $0.00151 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
hermes-loop-engineering 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 11d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes Loop Engineering
Core Principle
Turn delivery failures into reusable governance improvements only after evidence, observation, and forward-testing support the change.
This is human-in-the-loop self-improvement, not automatic model training.
When To Use
Use this skill after an intake, evidence-boundary, acceptance-scope, or owner-boundary review surfaces a pattern that may recur.
Typical triggers:
- the same kind of UAT reopen appears again,
- an agent repeats an evidence overclaim,
- a verifier repeatedly accepts beyond scope,
- owner routing fails for the same reason,
- a candidate rule may need to become wording, a template field, or a hard gate,
- a trial observation needs a keep / revise / promote / drop decision.
Loop
- Capture the delivery failure or disputed outcome.
- State the evidence boundary.
- Identify the smallest failed gate or missing evidence.
- Decide whether the signal is one-off, repeated, or systemic.
- Fill or reference a
trial_observation. - Choose the smallest improvement:
- keep as observation,
- revise wording,
- add or revise a template field,
- add an example,
- forward-test a candidate,
- consider a hard gate.
- Record false-positive, false-negative, and maintenance risks.
- Return keep / revise / promote / drop.
Output Contract
verdict:
failure_signal:
evidence_boundary:
candidate_pattern:
reuse_scope:
not_reuse_scope:
observed_repetition:
agent_rationalization:
recommended_change:
forward_test_needed:
forward_test_result:
behavior_change_evidence:
promotion_level:
false_positive_risk:
maintenance_cost:
rollback_condition:
next_owner:
must_not_claim:
Verdict Vocabulary
| Verdict | Use When |
|---|---|
record_observation |
The signal is useful but not yet reusable. |
revise_wording |
A light wording change can reduce a repeated mistake. |
revise_template |
A missing field or template shape causes repeated gaps. |
add_example |
A concrete scenario would teach the judgment better than a rule. |
forward_test_candidate |
The candidate looks useful but needs pressure testing. |
promote_to_hard_gate_candidate |
The pattern is repeated, high-risk, low-noise, and cheap to check. |
drop_candidate |
The candidate is too broad, noisy, stale, or misleading. |
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.
- 11d ago First seen · 106 lines · 32 tokens per session scan A 76c673f31ff2
hermes-loop-engineering is a skill published in the GitHub repository Gesondian/ai-collab-governance-skills (1 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 756 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 2026-08-31.
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