honcho
Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session…
Skill by Tommy-yw
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Use cases: finance
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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 26, 2026.
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What's inside
- Skill name
honcho- Description
- Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.
- License
- MIT
- Instructions
- 417 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# 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)
```bash
hermes honcho setup
# select "cloud", paste API key from https://app.honcho.dev
```
### Self-hosted
```bash
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
```bash
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:
1. **Session summary** -- a short digest of the current session so far (placed first so the model has immediate conversational continuity)
2. **User representation** -- Honcho's accumulated model of the user (preferences, facts, patterns)
3. **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 |
You do not need to configure this -- it is automatic based on session state.
### Peers
Honcho models conversations as interactions between **peers**. Hermes creates two peers per session:
- **User peer** (`peerName`): represents the human. Honcho builds a user representation from observed messages.
- **AI peer** (`aiPeer`): represents this Hermes 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`:
```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 |
Settings changed in the [Honcho dashboard](https://app.honcho.dev) are synced back on session init -- server-side config wins over local defaults.
### Sessions
Honcho sessions scope where messages and observations land. Strategy options:
| Strategy | Behavior |
|----------|----------|
| `per-directory` (default) | One session per working directory |
| `per-repo` | One session per git repository root |
| `per-session` | New Honcho session each Hermes run |
| `global` | Single session across all directories |
Manual override: `hermes honcho map my-project-name`
### Recall Modes
How the agent accesses Honcho memory:
| Mode | Auto-inject context? | Tools available? | Use case |
|------|---------------------|-----------------|----------|
| `hybrid` (default) | Yes | Yes | Agent decides when to use tools vs auto context |
| `context` | Yes | No (hidden) | Minimal token cost, no tool calls |From Tommy-yw/RunbookHermes, MIT.
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Signals
- Stars
- 510
- Forks
- 36
- Open issues
- 2
- Last commit (this folder)
- May 1, 2026 (6 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Tommy-yw/RunbookHermes
- Website
- github.com/Deepleaper
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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honcho skill. Agent Mart. https://agentmart.dev/skills/tommy-yw-honcho (last updated 2026-10-24)