---
name: local-timmy-overnight-loop
description: Deploy an unattended overnight loop that runs grounded tasks against local llama-server via Hermes, logging every result with timing. Produces rich capability data for morning review.
version: 1.0.0
author: Ezra
license: MIT
metadata:
  hermes:
    tags: [local-model, llama.cpp, overnight, data-generation, sovereignty, timmy]
    related_skills: [local-llama-tool-calling-debug, wizard-house-remote-triage]
---

# Local Timmy Overnight Loop

## When to Use

- Local Timmy needs to generate capability data overnight
- You want to measure tool-call success rates, response times, and failure modes
- The model is too slow for interactive use but can produce useful data unattended
- Issue #93 (proof test) needs empirical evidence from many runs

## Prerequisites

- llama-server running with `--jinja` flag (required for tool calls)
- Hermes agent installed at `~/.hermes/hermes-agent/`
- Timmy workspace at `~/.timmy/`
- Model path known (e.g., `/Users/apayne/models/hermes4-14b/NousResearch_Hermes-4-14B-Q4_K_M.gguf`)

## Key Design Decisions

### Strip the system prompt
The default Timmy system prompt is ~12K tokens (SOUL.md + skills list + memory). On a 14B Q4 model, this causes multi-minute prompt processing. The overnight loop uses a minimal prompt (~100 tokens):

```
You are Timmy. You run locally on llama.cpp.
You MUST use the tools provided. Do not narrate tool calls as text.
When asked to read a file, call the read_file tool.
When asked to write a file, call the write_file tool.
When asked to search, call the search_files tool.
Be brief. Do the task. Report what you found.
```

### Skip context files and memory
Pass `skip_context_files=True` and `skip_memory=True` to AIAgent to prevent injecting AGENTS.md, project context, skills, and memory into the prompt.

### Restrict toolsets
Each task specifies only the toolsets it needs (usually just `file`). Fewer tool schemas = less context = faster processing.

### Reduce context length and use single slot
Start llama-server with `-c 8192 -np 1` instead of `-c 65536`. The `-np 1` is critical — without it, llama-server defaults to 4 parallel slots, splitting 8192 into 2048 per slot. That's not enough for tool schemas + prompt, and the server silently hangs with `n_decoded: 0`. Single slot gives the full context to the loop's requests.

### Use the venv python
macOS system python is 3.9 which lacks `X | None` syntax. Always use `~/.hermes/hermes-agent/venv/bin/python3`.

## Script Location

Deploy to: `~/.timmy/scripts/timmy_overnight_loop.py`
Results in: `~/.timmy/overnight-loop/`

## Output Format

- `overnight_run_YYYYMMDD_HHMMSS.jsonl` — one JSON line per task with full result
- `overnight_summary_YYYYMMDD_HHMMSS.md` — rolling human-readable summary

Each JSONL entry contains:
```json
{
  "task_id": "read-soul",
  "run": 1,
  "started_at": "...",
  "finished_at": "...",
  "elapsed_seconds": 45.2,
  "status": "pass|empty|error",
  "response": "...",
  "session_id": "...",
  "provider": "custom",
  "base_url": "http://localhost:8081/v1",
  "model": "hermes4:14b",
  "prompt": "...",
  "error": null
}
```

## Task Design

Good overnight tasks are:
1. **Single tool call** — read one file, search one pattern
2. **Verifiable** — expected output is known (file exists, content is deterministic)
3. **Varied** — mix of read_file, write_file, search_files
4. **Grounded** — require actual file operations, not knowledge recall
5. **Short prompt** — under 100 words

Example tasks:
- "Read ~/.timmy/SOUL.md. Quote the first sentence of the Prime Directive."
- "Search ~/.hermes/bin/ for the string 'chatgpt.com'. Report which files."
- "Write a file to ~/.timmy/overnight-loop/timmy_wrote_this.md with content: ..."
- "Read ~/.hermes/config.yaml. What model is configured as default?"

## Starting the Loop

```bash
cd ~/.hermes/hermes-agent
nohup venv/bin/python3 ~/.timmy/scripts/timmy_overnight_loop.py \
  > ~/.timmy/overnight-loop/loop_stdout.log 2>&1 &
echo "PID: $!"
```

## Monitoring

```bash
# Check if running
pgrep -f timmy_overnight_loop

# Live progress
tail -f ~/.timmy/overnight-loop/loop_stdout.log

# Latest summary
cat ~/.timmy/overnight-loop/overnight_summary_*.md | tail -30

# Count completed tasks
wc -l ~/.timmy/overnight-loop/overnight_run_*.jsonl
```

## Stopping

```bash
pkill -f timmy_overnight_loop
```

## Morning Analysis

Key metrics to extract:
1. **Tool call success rate** — did the model actually use tools?
2. **Average response time** — baseline for performance tuning
3. **Error patterns** — which tasks fail and why?
4. **Pass/empty ratio** — empty responses mean the model responded but didn't use tools
5. **Time-series trend** — does performance degrade over cycles?

```bash
# Quick stats
python3 -c "
import json
results = [json.loads(l) for l in open('overnight_run_*.jsonl')]
passes = sum(1 for r in results if r['status'] == 'pass')
total = len(results)
avg = sum(r.get('elapsed_seconds',0) for r in results) / max(total,1)
print(f'Pass: {passes}/{total} ({100*passes//max(total,1)}%)')
print(f'Avg time: {avg:.1f}s')
print(f'Errors: {sum(1 for r in results if r[\"status\"]==\"error\")}')
"
```

## Pitfalls

1. **Kill stale hermes processes first.** Old stuck sessions compete for llama-server slots. Run `pkill -f "hermes chat"` before starting the loop. Also kill legacy loops: `pkill -f gemini-loop; pkill -f ops-dashboard; pkill -f timmy-status`.
2. **Also kill legacy loops.** gemini-loop.sh, ops-dashboard.sh, timmy-status.sh may be running. They waste resources.
3. **Check llama-server health before starting.** `curl -s http://localhost:8081/health` — if it's processing a stale request, restart it.
4. **The loop sleeps 30s between cycles.** This prevents hammering the model. Adjust if needed.
5. **Gemini fallback may silently activate.** If `fallback_model` in config.yaml points to Gemini, slow/failed local requests may route to cloud. Check config before running.
6. **Security guards block remote process kills.** If running remotely via SSH, `pkill` commands on the Mac may need user approval. Have Alexander run kill commands directly.
