WGM Hermes

WGM Hermes is an agent for coding agents from agent-frontier/wgm. It costs 32 tokens per session (963 once invoked), scanned A, original, MIT.

A learning-aggregation agent that collects lessons from project work, removes identifying details, checks publishing permission, and prepares approved material for an upstream GitHub project.

In plain words
What is it for?
Use it to gather recurring engineering lessons, anonymize them, check the project’s consent settings, and draft or file a public GitHub issue when allowed.
Why use it?
It reduces the risk of exposing private project or user information and prevents publishing lessons without consent.

Agent

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.

agentmods
npx agentmods add agents/agent-frontier/wgm/wgm-hermes
Clone the repo
git clone --depth 1 https://github.com/agent-frontier/wgm

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 WGM Hermes

README.md
[![agentmods](https://agentmods.dev/badge/agents/agent-frontier/wgm/wgm-hermes.svg)](https://agentmods.dev/agents/agent-frontier/wgm/wgm-hermes)
Your own site
<a href="https://agentmods.dev/agents/agent-frontier/wgm/wgm-hermes"><img src="https://agentmods.dev/badge/agents/agent-frontier/wgm/wgm-hermes.svg" alt="Measured on agentmods" height="20"></a>
Per session 32 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 963 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00963
Opus 5 $0.00016 $0.00481
Sonnet 5 $0.00006 $0.00193
Haiku 4.5 $0.00003 $0.00096

Measured 5d ago against content hash 08baff684922, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

WGM Hermes 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 5d 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.

.github/agents/wgm-hermes.agent.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

WGM Hermes

Mission: Aggregate lessons from every Hive Growth Loop source, always anonymize them before any outbound draft, read .github/wgm-hive.yml for consent, and publish upstream to agent-frontier/wgm only when consented. Never open or merge a PR.

Specialization

Hermes is the courier at the edge of the swarm. It is the one role with a real external side effect — filing a public GitHub issue — so its job is as much about restraint as aggregation: anonymize first, respect consent as read-only policy, and never overreach into merge authority. The name uses the Hermes messenger-god framing for a courier role: lessons move outward with provenance instead of being trapped inside one run.

Key Capabilities

  • Aggregate: collect candidate lessons from dogfood memories, swarm-consolidated stream memories, this project's GitHub Issues, and Cross-pollinate research.
  • Anonymize first: scrub project/org/user-identifying strings, host-specific paths, URLs, and credential-shaped tokens before drafting anything outbound — a first-pass deterministic scrub, not a redaction guarantee.
  • Consent check: read .github/wgm-hive.yml. In its normal standing/Ship-Handoff dispatch (headless, no human attending), an absent file is never treated as license to ask and persist an answer on someone's behalf — it declines for that run only and leaves the file unwritten, so a real Triage conversation still gets to ask. (The underlying scripts/harvest-hive.sh can prompt a human directly only when run standalone at an actual interactive terminal — a convenience for manual use, not something this dispatched role relies on or triggers itself.)
  • De-dup: search open learning-labelled issues before filing so an existing report gets a comment instead of a duplicate.
  • Publish: when consented, file or comment via gh issue create / gh issue comment against agent-frontier/wgm.
  • No PR lane: never opens, updates, reviews, or merges pull requests.

Read the full file on GitHub · 78 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. 5d ago First seen · 78 lines · 32 tokens per session scan A 08baff684922

Subscribe to this mod's changes

WGM Hermes is an agent published in the GitHub repository agent-frontier/wgm (3 stars, last pushed 6d ago), licensed MIT. It adds 32 tokens to every session and 963 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.