mlir-evolve

name	mlir-evolve
description	MLIR/LLVM/IREE workflow for the mlirAgent repo. Use when working in this repo to build or configure toolchains, analyze/debug passes (provenance/rlm), run evolutionary optimization, manage the Neo4j/LanceDB knowledge graph, or mine compiler recipes.

mlirAgent

Overview

Use this skill to navigate and extend the mlirAgent toolchain and research workflows.

Quick Start

• Reuse existing wrappers in src/mlirAgent/tools/*.py before creating new ones.
• Use src/mlirAgent/config.py for paths and external endpoints (Neo4j, LanceDB).
• Install the package with pip install -e . for proper imports (no sys.path hacks).

Core Capabilities

1) Build + Toolchain Management

• Use src/mlirAgent/tools/build.py for Ninja/CMake builds.
• Prefer Config-driven paths and flags.

2) Pass Analysis + Provenance

• Prefer the structural tracer in src/mlirAgent/tools/provenance.py.
• Use src/mlirAgent/tools/trace_provenance.py only when bindings are unavailable.
• For RLM-based analyses, see src/mlirAgent/rlm/analysis.py.

3) Evolutionary Optimization

• Evolve tasks live under src/mlirAgent/evolve/tasks/.
• Available tasks: llvm_inlining/, llvm_bench.py, regalloc_priority/.
• Configuration via configs/ directory.

4) Recipe Mining

• Mining pipeline in src/mlirAgent/mining/.
• Synthesize recipes with synthesize_recipes.py (requires a model_fn callable).

5) Knowledge Graph (SCIP + Neo4j)

• SCIP ingestion scripts in src/mlirAgent/scip/.
• Graph status, inspection, and impact analysis tools included.

Conventions

• Write outputs to data/ or data/artifacts/ to keep experiments reproducible.
• All file paths should come from Config or environment variables, not hardcoded.