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 nativ3ai/hermes-agent-camel --skill honchogit clone --depth 1 https://github.com/nativ3ai/hermes-agent-camelWrote 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/nativ3ai/hermes-agent-camel/honcho)<a href="https://agentmods.dev/skills/nativ3ai/hermes-agent-camel/honcho"><img src="https://agentmods.dev/badge/skills/nativ3ai/hermes-agent-camel/honcho/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/nativ3ai/hermes-agent-camel/honcho"><img src="https://agentmods.dev/badge/skills/nativ3ai/hermes-agent-camel/honcho.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 | $0.00065 | $0.04668 |
| Opus 5 | $0.00032 | $0.02334 |
| Sonnet 5 | $0.00013 | $0.00934 |
| Haiku 4.5 | $0.00006 | $0.00467 |
Grade A, and why
honcho 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.
This is a copy
98% identical to honcho — 7 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 — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honcho Memory for Hermes
Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Hermes profile its own peer identity while sharing a unified view of the user.
When to Use
- Setting up Honcho (cloud or self-hosted)
- Troubleshooting memory not working / peers not syncing
- Creating multi-profile setups where each agent has its own Honcho peer
- Tuning observation, recall, dialectic depth, or write frequency settings
- Understanding what the 5 Honcho tools do and when to use them
- Configuring context budgets and session summary injection
Setup
Cloud (app.honcho.dev)
hermes honcho setup
# select "cloud", paste API key from https://app.honcho.dev
Self-hosted
hermes honcho setup
# select "local", enter base URL (e.g. http://localhost:8000)
See: https://docs.honcho.dev/v3/guides/integrations/hermes#running-honcho-locally-with-hermes
Verify
hermes honcho status # shows resolved config, connection test, peer info
Architecture
Base Context Injection
When Honcho injects context into the system prompt (in hybrid or context recall modes), it assembles the base context block in this order:
- Session summary -- a short digest of the current session so far (placed first so the model has immediate conversational continuity)
- User representation -- Honcho's accumulated model of the user (preferences, facts, patterns)
- AI peer card -- the identity card for this Hermes profile's AI peer
The session summary is generated automatically by Honcho at the start of each turn (when a prior session exists). It gives the model a warm start without replaying full history.
Cold / Warm Prompt Selection
Honcho automatically selects between two prompt strategies:
| Condition | Strategy | What happens |
|---|---|---|
| No prior session or empty representation | Cold start | Lightweight intro prompt; skips summary injection; encourages the model to learn about the user |
| Existing representation and/or session history | Warm start | Full base context injection (summary → representation → card); richer system prompt |
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 · 431 lines · 65 tokens per session scan A e27066821bc2
honcho is a skill published in the GitHub repository nativ3ai/hermes-agent-camel (196 stars, last pushed 4mo ago), licensed MIT. It adds 65 tokens to every session and 4,668 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to honcho, differing in 7 lines, and is treated as a copy.
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