Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/microsoft/flaml/copilot-instructionsgit clone --depth 1 https://github.com/microsoft/FLAMLWhat 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 | $0.01772 | $0.01772 |
| Opus 5 | $0.00886 | $0.00886 |
| Sonnet 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 2d 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.
- 2d 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 (4,391 stars, last pushed 4d ago), licensed MIT. It adds 1,772 tokens to every session, about $0.0089 per session on Opus 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.
Other instructions, from other repositories
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marimo copilot-instructions.md
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mlops-python-package AGENTS.md
AGENTS.md instructions for fmind/mlops-python-package, covering agents.md, project overview, setup & core commands, definition of done and conventions & idioms.
pycaret AGENTS.md
AGENTS.md instructions for pycaret/pycaret, covering agents.md — pycaret agent instructions, tl;dr — the 60-second briefing, start here, non-negotiables and universal rules.
pycaret CLAUDE.md
Claude Code instructions for pycaret/pycaret, covering claude.md — claude code guide for this repo, at a glance, how contributors use this repo with claude code, files claude code reads (in order) and common commands.
scomp_link AGENTS.md
Instructions for GiacomoSaccaggi/scomp_link, covering agents.md — instructions for ai coding agents, what is scomp-link?, when to use scomp-link, cli quick reference (26 commands) and data understanding.