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 braxtonROSE4/zorro-agent --skill honchogit clone --depth 1 https://github.com/braxtonROSE4/zorro-agentWrote 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/braxtonrose4/zorro-agent/honcho)<a href="https://agentmods.dev/skills/braxtonrose4/zorro-agent/honcho"><img src="https://agentmods.dev/badge/skills/braxtonrose4/zorro-agent/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/braxtonrose4/zorro-agent/honcho"><img src="https://agentmods.dev/badge/skills/braxtonrose4/zorro-agent/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 | $0.00055 | $0.02406 |
| Opus 5.5 | $0.00022 | $0.00962 |
| Sonnet 5.5 | $0.00011 | $0.00481 |
| Haiku 4.5 | $0.00006 | $0.00241 |
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 1mo 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 — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Honcho Memory for Zorro
Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Zorro 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, or write frequency settings
- Understanding what the 4 Honcho tools do and when to use them
Setup
Cloud (app.honcho.dev)
zorro honcho setup
# select "cloud", paste API key from https://app.honcho.dev
Self-hosted
zorro honcho setup
# select "local", enter base URL (e.g. http://localhost:8000)
See: https://docs.honcho.dev/v3/guides/integrations/zorro#running-honcho-locally-with-zorro
Verify
zorro honcho status # shows resolved config, connection test, peer info
Architecture
Peers
Honcho models conversations as interactions between peers. Zorro creates two peers per session:
- User peer (
peerName): represents the human. Honcho builds a user representation from observed messages. - AI peer (
aiPeer): represents this Zorro instance. Each profile gets its own AI peer so agents develop independent views.
Observation
Each peer has two observation toggles that control what Honcho learns from:
| Toggle | What it does |
|---|---|
observeMe |
Peer's own messages are observed (builds self-representation) |
observeOthers |
Other peers' messages are observed (builds cross-peer understanding) |
Default: all four toggles on (full bidirectional observation).
Configure per-peer in honcho.json:
{
"observation": {
"user": { "observeMe": true, "observeOthers": true },
"ai": { "observeMe": true, "observeOthers": true }
}
}
Or use the shorthand presets:
| Preset | User | AI | Use case |
|---|---|---|---|
"directional" (default) |
me:on, others:on | me:on, others:on | Multi-agent, full memory |
"unified" |
me:on, others:off | me:off, others:on | Single agent, user-only modeling |
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.
- 1mo ago First seen · 244 lines · 55 tokens per session scan A dc69457c8765
honcho is a skill published in the GitHub repository braxtonROSE4/zorro-agent (10 stars, last pushed 6mo ago), licensed MIT. It adds 55 tokens to every session and 2,406 once invoked, about $0.0002 per session on Opus 5.5. 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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openlore-housekeeping
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semantix
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memory-flush
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skills-constitution
A set of rules that makes an agent check its memory and available skills before handling professional tasks such as coding, file work, data analysis, or deployment.
narco-check
Memory integrity audit. Detects hallucinations, circular confirmations, and state poisoning. Runs automatically after 2 consecutive failures or at nightly deep dive. Uses Opus 4.6 as the auditor model.