优化特工技能，比如梯度下降。

SkillGrad 将客服技能包视为结构化参数， 通过文本上的梯度下降对应物进行优化：执行者 生成轨迹，诊断器将其转化为每个任务的更新 动量因素在信号中积累了反复出现的模式 迭代后，补丁程序会对技能进行层感知编辑。

优化类比：

仓库布局

安装

该仓库包含完整的 SpreadsheetBench 验证集成作为参考流水线。运行时，按照 data/benchmarks/README.md 中的布局将数据集置于 data/benchmarks/spreadsheetbench/ 下。要使用 SkillGrad 和新的基准测试，需实现相应的加载器和评分界面。

快速开始

SkillGrad

Optimizing agent skills like gradient descent.

SkillGrad treats an agent skill package as a structured parameter and optimizes it through a textual analogue of gradient descent: an executor produces trajectories, a diagnoser turns them into per-task update signals, a momentum agent accumulates recurring patterns across iterations, and a patcher applies a layer-aware edit to the skill.

The optimization analogy:

Repository layout

pipeline/        executor, diagnoser, momentum, patcher, training loop
prompts/         the five agent prompts
runners/         model dispatch, cost ledger, trajectory logger, …
evaluators/      xlsx grader used as the terminal loss signal
data/            dataset loader, split, layout
scripts/         base_traj.sh, train.sh, eval.sh
seeds/xlsx/      bundled initial skill (SKILL.md)
assets/          figures referenced from the README

Installation

pip install -r requirements.txt

cp .env.example .env
# fill in exactly one of the two provider blocks

This repo includes a complete SpreadsheetBench Verified integration as a reference pipeline. To run it, place the dataset under data/benchmarks/spreadsheetbench/ following the layout in data/benchmarks/README.md. To use SkillGrad with a new benchmark, implement the corresponding loader and grader interfaces.

Quickstart

Three commands run an end-to-end optimization cycle from the repo root.

bash scripts/base_traj.sh   # 1. collect failure pool
bash scripts/train.sh       # 2. train SkillGrad
bash scripts/eval.sh        # 3. evaluate on held-out test split

Switch the backbone model via the MODEL environment variable:

MODEL=gpt-4.1 bash scripts/train.sh

The default run id is skillgrad_. Final artifacts land under results/runs/skillgrad_/:

results/runs/skillgrad_gpt-5.4/
├── config.json
├── train/
│   ├── training_results.json
│   ├── iter_1/, iter_2/, …
│   └── final_skill/xlsx/
└── eval/
    ├── summary.json
    └── /trajectory.json

Initial skill

The training loop optimizes whatever initial skill you point --skills-dir at. The repository ships with one bundled seed at seeds/xlsx/SKILL.md and the scripts default to it. To use a different initialization, lay it out as /xlsx/SKILL.md and pass SKILLS_DIR= to the scripts.

Configuration