claudini

Category: Research & Analysis | Uploader: romovpa | Downloads: 0 | Version: v1.0（Latest）

Run one iteration of the autoresearch loop — study existing attack methods, design a better optimizer, implement it, benchmark it, and commit. Meant to be called repeatedly via /loop.

Changelog: Source: GitHub https://github.com/romovpa/claudini

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Current level: tree/main/.claude/skills/claudini/

📄 SKILL.md 3.2 KB

SKILL.md

---
name: claudini
description: Run one iteration of the autoresearch loop — study existing attack methods, design a better optimizer, implement it, benchmark it, and commit. Meant to be called repeatedly via /loop.
argument-hint: "run_code goal — e.g. safeguard break Qwen2.5-7B under 1e15 FLOPs"
---

# Autoresearch Iteration

You are an automated researcher designing token optimization methods to minimize token-forcing loss on language models.

- **Run code**: `$ARGUMENTS[0]` — determines the method chain, branch, and log location
- **Goal** (everything after the run code): the research objective

This skill runs ONE iteration of the research loop. It is designed to be called repeatedly via `/loop`.

**Derived from run code `$ARGUMENTS[0]`:**
- Method directory: `claudini/methods/claude_$ARGUMENTS[0]/`
- Method name prefix: `claude_$ARGUMENTS[0]_v`
- Git branch: `loop/$ARGUMENTS[0]`
- Agent log: `claudini/methods/claude_$ARGUMENTS[0]/AGENT_LOG.md`

## Initialization (first iteration only)

Read `claudini/methods/claude_$ARGUMENTS[0]/AGENT_LOG.md`. If it exists, skip this section — the run is already set up.

**Config.** If the user's goal mentions a specific config name (e.g. `random_train`, `safeguard_valid`), use that existing config from `configs/`. Otherwise, check `configs/` for a preset that matches. Only create a new config if nothing fits:

```yaml
# Autoresearch: <brief description>
model: <model_id>
optim_length: 15
max_flops: <budget>
dtype: bfloat16
system_prompt: ""
samples: [0, 1, 2]
seeds: [0]
final_input: tokens
use_prefix_cache: true

input_spec:
  source:
    type: random
    query_len: 0
    target_len: 10
  layout:
    type: suffix
  init:
    type: random
```

Parse the goal to extract model (default: `Qwen/Qwen2.5-7B-Instruct`) and FLOP budget (default: `1.0e+15`).

**Git branch.** Create and switch to `loop/$ARGUMENTS[0]` if not already on it.

**Agent log.** Create `claudini/methods/claude_$ARGUMENTS[0]/AGENT_LOG.md` with the config name, goal, and setup details.

## Step 1 — Design and implement a new method

Design and implement a new optimizer that achieves lower loss than existing methods. Read the agent log, then use whatever you need:

- Agent log: `claudini/methods/claude_$ARGUMENTS[0]/AGENT_LOG.md`
- Your method chain: `claudini/methods/claude_$ARGUMENTS[0]/`
- Other methods: `claudini/methods/` (baselines and other Claude-designed chains)
- Benchmark results: `results/` (shared across all runs and methods)
- Developer guide: `CLAUDE.md`

Create the next version as a proper Python package under `claudini/methods/claude_$ARGUMENTS[0]/v<N>/` with `method_name = "claude_$ARGUMENTS[0]_v<N>"`.

## Step 2 — Run the benchmark

The method must not override config settings — suffix length, FLOP budget, model, samples, etc. are controlled by the config, not the optimizer.

Run the full benchmark. Launch in background and don't wait:
```bash
uv run -m claudini.run_bench <config> --method claude_$ARGUMENTS[0]_v<N>
```

## Step 3 — Commit and update log

Commit the new method and any config changes to the `loop/$ARGUMENTS[0]` branch. Then update `claudini/methods/claude_$ARGUMENTS[0]/AGENT_LOG.md` with:
- What method you created and the key idea
- What to try next iteration

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