SkillOpt: NL2SQL Local Trainer

A streamlined, community-optimized fork of Microsoft's SkillOpt, stripped of all extra benchmarks and hard-coded to train your local models (like Qwen via Ollama) to become PostgreSQL experts using text-space optimization.

---

🛠️ What is this Fork?

This repository takes the powerful SkillOpt engine (which automatically rewrites system prompts to improve model performance) and turns it into a dedicated NL2SQL (Natural Language to SQL) trainer.

1. No Azure Lock-in: The original azure_openai.py wrapper has been bypassed. You can use standard OpenAI endpoints (like DeepSeek via Polza.ai, Ollama, vLLM).
2. Pure SQL Focus: All other heavy benchmarks (ALFWorld, SweBench, etc.) have been removed. The evaluator is hard-coded to compare SQL syntax accurately.
3. UTF-8 Support: Fixed UnicodeDecodeError when reading/writing skill files containing non-ASCII (e.g., Russian) characters.

---

Install

Requirements: Python 3.10+

git clone https://github.com/SolarisStation/SkillOpt-NL2SQL.git
cd SkillOpt-NL2SQL
python -m venv venv
source venv/bin/activate # On Windows: .\venv\Scripts\activate
pip install -r requirements.txt
pip install -e .

---

Quick Start Configuration

All configuration is done in configs/nl2sql/default.yaml.

# Core environment settingsenv: "nl2sql"split_mode: "split_dir"split_dir: "data/nl2sql"# Target Model (e.g., Local Ollama student)target_model:
model: "qwen3.5:2b"api_type: "openai"endpoint: "http://localhost:11434/v1"api_key: "ollama"auth_mode: "api_key"temperature: 0.0# Optimizer Model (e.g., DeepSeek cloud teacher)optimizer_model:
model: "deepseek/deepseek-v4-flash"api_type: "openai"endpoint: "https://api.polza.ai/v1"# Or https://api.deepseek.com/v1api_key: "YOUR_API_KEY"auth_mode: "api_key"temperature: 0.1# Training parametersnum_epochs: 3batch_size: 5accumulation: 1seed: 42merge_batch_size: 5edit_budget: 4min_edit_budget: 2validation_interval: 1# Evaluation parameterssel_env_num: 2test_env_num: 1eval_val: trueeval_test: trueskill_init: "configs/nl2sql/initial_skill.md"out_root: "outputs/nl2sql"

Run Training

python scripts/train.py --config configs/nl2sql/default.yaml

---

Data Preparation

Provide your NL2SQL examples in data/nl2sql/train, val, and test directories using this JSON schema:

[
{
"id": "1",
"question": "Сколько пользователей зарегистрировано в системе?",
"answers": ["SELECT COUNT(*) FROM users;"]
}
]

| --out_root | Output directory | outputs/my_run |

Eval Only

Evaluate a trained skill on specific data splits without training:

# Evaluate on test set only:
python scripts/eval_only.py \
--config configs/searchqa/default.yaml \
--skill outputs/my_run/best_skill.md \
--split valid_unseen \
--split_dir /path/to/searchqa_split \
--azure_openai_endpoint https://your-resource.openai.azure.com/
# Evaluate on all splits (train + val + test):
python scripts/eval_only.py \
--config configs/searchqa/default.yaml \
--skill outputs/my_run/best_skill.md \
--split all \
--split_dir /path/to/searchqa_split \
--azure_openai_endpoint https://your-resource.openai.azure.com/

Split	Description
valid_unseen	Test set
valid_seen	Validation set
train	Training set
all	All splits combined (default)

Output Structure

Each run writes to a structured output directory:

outputs/<run_name>/
├── config.json # Flattened runtime config
├── history.json # Per-step training history
├── runtime_state.json # Resume checkpoint
├── best_skill.md # Best validated skill document
├── skills/skill_vXXXX.md # Skill snapshot per step
├── steps/step_XXXX/ # Per-step artifacts (patches, evals)
├── slow_update/epoch_XX/ # Slow update logs
└── meta_skill/epoch_XX/ # Meta skill logs

Re-running the same command auto-resumes from the last completed step.

---

WebUI

Launch the monitoring dashboard (optional):

pip install -e ".[webui]"
python -m skillopt_webui.app

Flag	Default	Description
--port	7860	Server port
--host	0.0.0.0	Bind address
--share	off	Create a public Gradio share link

# With public share link (useful for remote servers)
python -m skillopt_webui.app --share

---

Citation

@misc{yang2026skilloptexecutivestrategyselfevolving,
title={SkillOpt: Executive Strategy for Self-Evolving Agent Skills}, author={Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},
year={2026},
eprint={2605.23904},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.23904}
}