hermes-loop-engineering

hermes-loop-engineering is a skill for Claude Code, Codex from Gesondian/ai-collab-governance-skills. It costs 32 tokens per session (756 once invoked), scanned A, original, MIT.

A review method for turning repeated delivery problems into small, reusable process improvements. It uses observations and forward testing rather than automatically retraining the AI.

In plain words
What is it for?
It is for recording delivery failures, deciding whether a pattern is recurring, and improving wording, templates, examples, or approval checks.
Why use it?
It helps teams avoid repeating the same acceptance, evidence, routing, or verification failures.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for recording delivery failures, deciding whether a pattern is recurring, and improving wording, templates, examples, or approval checks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering
Install

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.

Any agent
npx skills add Gesondian/ai-collab-governance-skills --skill hermes-loop-engineering
Clone the repo
git clone --depth 1 https://github.com/Gesondian/ai-collab-governance-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for hermes-loop-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering/github.svg)](https://agentmods.dev/skills/gesondian/ai-collab-governance-skills/hermes-loop-engineering)
Your own site
<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.

agentmods 80×15 button for hermes-loop-engineering

Your own site · 80×15
<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>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 756 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 76c673f31ff2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/hermes-loop-engineering/SKILL.md · 106 lines

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

  1. Capture the delivery failure or disputed outcome.
  2. State the evidence boundary.
  3. Identify the smallest failed gate or missing evidence.
  4. Decide whether the signal is one-off, repeated, or systemic.
  5. Fill or reference a trial_observation.
  6. 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.
  7. Record false-positive, false-negative, and maintenance risks.
  8. 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.

Read the full file on GitHub · 106 lines

Changes

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.

  1. 11d ago First seen · 106 lines · 32 tokens per session scan A 76c673f31ff2

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

humanizar

Reescreve textos em português brasileiro para soarem mais humanos e naturais, reduzindo padrões típicos de escrita gerada por IA sem alterar fatos, argumento ou intenção. Use quando o texto em PT-BR parecer genérico, burocrático ou gerado por IA, ou quando o usuário pedir para "humanizar", "dar vida", "tirar cara de…

fabricioctelles/skills · 167 tokens

agent-ready-cloudflare

Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent…

fabricioctelles/skills · 158 tokens

human-ai

Rewrites English text so it reads as written by a person, without AI writing tics. Removes machine language patterns and AI slop, restores semantic entropy, and injects voice and personality. Use when ENGLISH text reads as generic, bland, or AI-generated - or when asked to "humanize", "de-slop", "remove AI patterns"…

fabricioctelles/skills · 115 tokens

coolify-operator

Master Coolify operator for self-hosted deployment platform. Use when the user mentions 'coolify', 'deploy on coolify', 'list/restart/redeploy applications', 'view coolify logs', 'coolify API/CLI', 'manage coolify servers/databases/apps', or 'coolify context'. Automates deployments and management via REST API or…

fabricioctelles/skills · 79 tokens

okf-open-knowledge-format

Create, validate, and enrich Open Knowledge Format (OKF) bundles — the open spec for representing organizational knowledge as markdown files with YAML frontmatter. Use when the user mentions 'OKF', 'Open Knowledge Format', 'knowledge bundle', 'OKF bundle', 'create a knowledge base for agents', 'validate OKF', 'convert…

fabricioctelles/skills · 168 tokens

resume-ats-beater

Este skill deve ser usado para reescrever currículos com foco em compatibilidade ATS e impacto para recrutadores, e/ou auditar perfis LinkedIn para maximizar visibilidade e conversão profissional. Acionar em pedidos de otimização de currículo, melhoria para ATS, reescrita profissional do CV, adaptação para vaga-alvo…

fabricioctelles/skills · 120 tokens