Chapter: index (docs/index.md) Copy Section

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SkillOpt

Train Agent Skills Like Neural Networks Copy Section

Optimize natural-language skill documents through iterative rollout, reflection, and gated validation — with epochs, learning rates, and validation gates — without touching model weights.

Get Started :material-rocket-launch:{ .md-button .md-button--primary }
View on GitHub :material-github:{ .md-button }

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Two Complementary Workflows Copy Chapter

| Workflow | Package / command | Use it for |
|---|---|---|
| Research engine | skillopt, skillopt-train, skillopt-eval | Train and evaluate skill documents on explicit benchmark splits. |
| SkillOpt-Sleep (preview) | skillopt_sleep, skillopt-sleep | Review supported coding-agent sessions and stage proposed memory/skill updates for human adoption. |

They share the idea of bounded text updates and validation, but they are
separate entry points with different configs and safety boundaries. Start with
the SkillOpt-Sleep overview before using real session data.

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How It Works Copy Chapter

<div class="pipeline-container" markdown>
<div class="pipeline-wrapper">

<div class="pipeline-stage" id="stage-rollout">
<div class="stage-icon">🎯</div>
<div class="stage-label">Rollout</div>
<div class="stage-desc">Target executes tasks</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>

<div class="pipeline-stage" id="stage-reflect">
<div class="stage-icon">🔍</div>
<div class="stage-label">Reflect</div>
<div class="stage-desc">Optimizer analyzes trajectories</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>

<div class="pipeline-stage" id="stage-aggregate">
<div class="stage-icon">🔗</div>
<div class="stage-label">Aggregate</div>
<div class="stage-desc">Merge edit patches</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>

<div class="pipeline-stage" id="stage-select">
<div class="stage-icon">✂️</div>
<div class="stage-label">Select</div>
<div class="stage-desc">Rank & clip edits</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>

<div class="pipeline-stage" id="stage-update">
<div class="stage-icon">📝</div>
<div class="stage-label">Update</div>
<div class="stage-desc">Apply to skill doc</div>
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<div class="pipeline-arrow"><div class="flow-line"></div></div>

<div class="pipeline-stage" id="stage-gate">
<div class="stage-icon">🚦</div>
<div class="stage-label">Gate</div>
<div class="stage-desc">Validate & accept</div>
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<div class="pipeline-epoch-bar">
<div class="epoch-mechanism">🔄 Slow Update</div>
<div class="epoch-mechanism">🧠 Meta Skill</div>
<div class="epoch-label">Epoch Boundary</div>
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Deep Learning Analogy Copy Chapter

SkillOpt brings the familiar deep-learning training paradigm to agentic prompt optimization:

| Deep Learning | SkillOpt |
|---|---|
| Model weights | Skill document (Markdown) |
| Forward pass | Rollout (target executes tasks) |
| Loss / gradient | Reflect (optimizer produces edit patches) |
| Gradient clipping | Edit selection (learning_rate = max edits) |
| SGD step | Patch application to skill |
| Validation set | Gated evaluation on selection split |
| LR schedule | lr_scheduler: cosine, linear, constant |
| Epochs | Multi-epoch with slow update & meta skill memory |

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Supported Benchmarks Copy Chapter

| Benchmark | Type | Config |
|---|---|---|
| DocVQA | Document QA | configs/docvqa/ |
| ALFWorld | Embodied AI | configs/alfworld/ |
| OfficeQA | Enterprise QA | configs/officeqa/ |
| SearchQA | Open-domain QA | configs/searchqa/ |
| LiveMathematicianBench | Math reasoning | configs/livemathematicianbench/ |
| SpreadsheetBench | Spreadsheet editing | configs/spreadsheetbench/ |

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Model Backends Copy Chapter

Optimizer and target roles are configured separately. Chat backends include
Azure OpenAI (openai_chat), the provider-neutral
openai_compatible backend, the Claude Code CLI (claude_chat), Qwen, and
MiniMax. Codex and Claude Code exec harnesses are target-only and require
adapter support. Despite its name, claude_chat launches claude -p; it is
not a direct Anthropic API client.

If a provider implements OpenAI Chat Completions, begin with the
built-in compatible backend
instead of adding a new integration. See Configuration
for authentication and per-role overrides.

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Quick Example Copy Chapter

Clone and install the research checkout plus the SearchQA data extra

git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt
python -m pip install -e ".[searchqa]"
Configure credentials (choose one auth mode in .env)

cp .env.example .env
set -a; source .env; set +a
Materialize the runnable split from the checked-in ID manifest

python scripts/materialize_searchqa.py
Train on SearchQA into a predictable output directory

python scripts/train.py \
  --config configs/searchqa/default.yaml \
  --out_root outputs/searchqa_quickstart
Evaluate best skill

python scripts/eval_only.py \
  --config configs/searchqa/default.yaml \
  --skill outputs/searchqa_quickstart/best_skill.md \
  --split valid_unseen

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- :material-book-open-variant:{ .lg .middle } Getting Started

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Install SkillOpt, configure your API keys, and run your first experiment in 5 minutes.

:octicons-arrow-right-24: Installation

- :material-puzzle:{ .lg .middle } Add a Benchmark

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Extend SkillOpt with your own benchmark in ~100 lines of code.

:octicons-arrow-right-24: Extension Guide

- :material-cog:{ .lg .middle } Configuration

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Full reference for all hyperparameters with deep learning analogies.

:octicons-arrow-right-24: Config Reference

- :material-monitor-dashboard:{ .lg .middle } WebUI

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Configure, launch, and monitor training from your browser.

:octicons-arrow-right-24: WebUI Guide

- :material-weather-night:{ .lg .middle } SkillOpt-Sleep

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Test the deployment companion with the no-provider mock path, then review
its data boundary before selecting a real backend.

:octicons-arrow-right-24: Sleep Overview

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