FLAML is a Python library that automates machine-learning tasks such as choosing models and tuning their hyperparameters under resource constraints. Data scientists use it to optimize machine-learning and AI workflows, including classification, regression, foundation-model inference, and MLOps configurations. The catalogue instruction helps users work with FLAML.
Borrowing it
Nothing to install: this file belongs to microsoft/FLAML. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/FLAML/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/microsoft/FLAMLWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/microsoft/flaml/copilot-instructions)<a href="https://agentmods.dev/instructions/microsoft/flaml/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/microsoft/flaml/copilot-instructions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/microsoft/flaml/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/microsoft/flaml/copilot-instructions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01772 | $0.01772 |
| Opus 5.5 | $0.00709 | $0.00709 |
| Sonnet 5.5 | $0.00354 | $0.00354 |
| Haiku 4.5 | $0.00177 | $0.00177 |
Grade A, and why
FLAML copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instructions for FLAML
Project Overview
FLAML (Fast Library for Automated Machine Learning & Tuning) is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.
Key Components:
flaml/automl/: AutoML functionality for classification and regressionflaml/tune/: Generic hyperparameter tuningflaml/default/: Zero-shot AutoML with default configurationsflaml/autogen/: Legacy autogen code (note: AutoGen has moved to a separate repository)flaml/fabric/: Microsoft Fabric integrationtest/: Comprehensive test suite
Build and Test Commands
Installation
# Basic installation
pip install -e .
# Install with test dependencies
pip install -e .[test]
# Install with automl dependencies
pip install -e .[automl]
# Install with forecast dependencies (Linux only)
pip install -e .[forecast]
Running Tests
# Run all tests (excluding autogen)
pytest test/ --ignore=test/autogen --reruns 2 --reruns-delay 10
# Run tests with coverage
coverage run -a -m pytest test --ignore=test/autogen --reruns 2 --reruns-delay 10
coverage xml
# Check dependencies
python test/check_dependency.py
Linting and Formatting
# Run pre-commit hooks
pre-commit run --all-files
# Format with black (line length: 120)
black . --line-length 120
# Run ruff for linting and auto-fix
ruff check . --fix
Code Style and Formatting
Python Style
- Line length: 120 characters (configured in both Black and Ruff)
- Formatter: Black (v23.3.0+)
- Linter: Ruff with Pyflakes and pycodestyle rules
- Import sorting: Use isort (via Ruff)
- Python version: Supports Python >= 3.10 (full support for 3.10, 3.11, 3.12 and 3.13)
Code Quality Rules
- Follow Black formatting conventions
- Keep imports sorted and organized
- Avoid unused imports (F401) - these are flagged but not auto-fixed
- Avoid wildcard imports (F403) where possible
- Complexity: Max McCabe complexity of 10
- Use type hints where appropriate
- Write clear docstrings for public APIs
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 244 lines · 1,772 tokens per session scan A 2cf8fa6effc8
FLAML copilot-instructions.md is an instructions file published in the GitHub repository microsoft/FLAML (4,416 stars, last pushed today), licensed MIT. It adds 1,772 tokens to every session, about $0.0071 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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