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.claude/skills/honcho-cli/SKILL.md

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---
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name: honcho-cli
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description: Inspect and debug Honcho workspaces via the `honcho` CLI. Use when investigating peer representations, memory state, session context, queue status, or dialectic quality — any task that requires introspection of a Honcho deployment.
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allowed-tools: Bash(honcho:*), Bash(jq:*), Read, Grep
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---
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# Honcho CLI
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`honcho` wraps the Honcho Python SDK with agent-friendly defaults: JSON output, structured errors, input validation. Use it to inspect workspace state, debug peer memory, and diagnose the dialectic.
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## Output & config
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- **TTY**: human-readable tables (default when interactive)
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- **Piped / `--json`**: JSON — collection commands emit arrays, single-resource commands emit objects
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- **Exit codes**: `0` success · `1` client error (bad input, not found) · `2` server error · `3` auth error
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- **Config**: `~/.honcho/config.json` (shared with other Honcho tools). The CLI owns `apiKey` and `environmentUrl` at the top level; run `honcho init` to confirm or set them. Per-command scope (workspace / peer / session) is via `-w` / `-p` / `-s` flags or `HONCHO_*` env vars.
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## Command groups
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- `honcho config` — CLI configuration
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- `honcho workspace` — inspect, delete, search
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- `honcho peer` — inspect, card, chat, search
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- `honcho session` — inspect, messages, context, summaries
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- `honcho message` — list and get
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- `honcho conclusion` — list, search, create, delete
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## Rules
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- Always pass `--json` when processing output programmatically.
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- Run `honcho peer inspect` before `honcho peer chat` to understand context.
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- Use `honcho session context` to see exactly what an agent receives.
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- Never run `honcho workspace delete` without `honcho workspace inspect` first.
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- Check queue status when derivation seems stalled.
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- Compare peer card with conclusions to understand memory state.
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## Inspection tour
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When orienting to a Honcho deployment, walk outside-in:
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### 1. Understand the workspace
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```bash
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honcho workspace inspect --json
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```
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### 2. Find the peer
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```bash
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honcho peer list --json
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honcho peer inspect <peer_id> --json
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```
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### 3. Check peer's memory
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```bash
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honcho peer card <peer_id> --json
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honcho conclusion list --observer <peer_id> --json
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honcho conclusion search "topic" --observer <peer_id> --json
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```
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### 4. Debug a session
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```bash
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honcho session inspect <session_id> --json
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honcho message list <session_id> --last 20 --json
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honcho session context <session_id> --json
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honcho session summaries <session_id> --json
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```
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### 5. Search across workspace
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```bash
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honcho workspace search "query" --json
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honcho peer search <peer_id> "query" --json
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```
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## Debugging playbook
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### Peer not learning?
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```bash
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# Is observation enabled?
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honcho peer inspect <peer_id> --json | jq '.configuration'
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# Is the deriver queue processing messages?
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honcho workspace queue-status --json
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# What conclusions exist?
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honcho conclusion list --observer <peer_id> --json
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honcho conclusion search "expected topic" --observer <peer_id> --json
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```
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### Session context looks wrong?
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```bash
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# Raw context an agent would receive
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honcho session context <session_id> --json
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# Summaries feeding the context
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honcho session summaries <session_id> --json
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# Recent message history
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honcho message list <session_id> --last 50 --json
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```
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### Dialectic giving bad answers?
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```bash
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# What the peer card says
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honcho peer card <peer_id> --json
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# Conclusions on the specific topic
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honcho conclusion search "topic" --observer <peer_id> --json
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# Exercise the dialectic directly
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honcho peer chat <peer_id> "what do you know about X?" --json
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```

.claude/skills/honcho-integration/SKILL.md

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### Phase 4: Verification
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- If the Honcho CLI is available, run `honcho doctor` to confirm connectivity before testing the integration code
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- Use `honcho peer list` and `honcho peer chat` to verify peers exist and the dialectic endpoint works independently of the integration
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- Ensure all message exchanges are stored to Honcho
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- Verify AI peers have `observe_me=False` (unless user specifically wants AI observation)
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- Check that the workspace ID is consistent across the codebase
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2. **Get an API key** ask the user to get a Honcho API key from <https://app.honcho.dev> and add it to the environment.
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3. **Verify with the CLI** (optional but recommended). If the user has the Honcho CLI installed (`pip install honcho-cli`), they can validate their setup before writing any integration code:
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```bash
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honcho init # persist API key + URL to ~/.honcho/config.json
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honcho doctor # verify connectivity, config, workspace health
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honcho peer chat # test the dialectic endpoint interactively
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```
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This is the fastest way to confirm the API key and URL are correct before debugging SDK code.
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## Installation
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### Python (use uv)
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- [ ] Pre-fetch pattern for simpler integrations
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- [ ] context() for conversation history
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- [ ] Store messages after each exchange to build user models
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- [ ] (Optional) Run `honcho doctor` to verify connectivity before testing integration code
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- [ ] (Optional) Use `honcho peer chat` to test dialectic queries independently
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## Common Mistakes to Avoid
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