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onnx2coreml: Instructions file for Codex

AGENTS.md

onnx2coreml AGENTS.md is an instructions file for Codex, OpenCode from devin-lai/onnx2coreml. It costs 831 tokens per session, scanned A, original, BSD-3-Clause.

Repository instructions for contributors to onnx2coreml, a tool that converts ONNX machine-learning models into Apple Core ML model files.

In plain words
What is it for?
Use it when adding support for an ONNX operator, changing conversion behavior, or writing related tests.
Why use it?
It explains the converter's pipeline and code boundaries, helping changes stay consistent and testable.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md.

This is devin-lai/onnx2coreml's own configuration. It tells Codex and OpenCode how to work on onnx2coreml itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything onnx2coreml configures →

not rated 149repo +2 4mo ago A scan 831 tokens original BSD-3-Clause
View source ↗ devin-lai/onnx2coreml
Reuse

Borrowing it

Nothing to install: this file belongs to devin-lai/onnx2coreml. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/devin-lai/onnx2coreml/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/devin-lai/onnx2coreml

Made for: Codex, OpenCode.

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Per session 831 This file is loaded in full into every session.
When invoked 831 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00831 $0.00831
Opus 5.5 $0.00332 $0.00332
Sonnet 5.5 $0.00166 $0.00166
Haiku 4.5 $0.00083 $0.00083

Measured 2mo ago against content hash e9173382e2b8, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

onnx2coreml AGENTS.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.

AGENTS.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Developer guide for onnx2coreml

Guidance for contributors working in this repo. Keep changes surgical, tested, and consistent with the patterns below.

What this is

An ONNX → Core ML converter producing .mlpackage (ML Program / MIL) and .mlmodel (NeuralNetwork), built on top of coremltools' MIL builder.

Architecture (one pass through the pipeline)

ONNX → _io.load → _passes.run → _fusion.run → coverage-gate → Converter.to_mil
     → _backend.program_to_mlmodel → MLModel (.mlpackage / .mlmodel) → _verify
  • _mil.py and _backend.py are the only modules that import coremltools. Keep it that way — this is how the project stays maintainable as Core ML evolves.
  • converter.py orchestrates: coverage gate, then a topological walk emitting MIL ops.
  • _target.py maps deployment-target/format/precision strings to coremltools enums. The MIL function is authored at iOS17 for .mlpackage and iOS15 for .mlmodel (the NeuralNetwork backend rejects newer opsets).

Adding an operator lowering

  1. Pick the right family module under src/onnx2coreml/_lowering/ (e.g. _conv.py).
  2. Write def lower(ctx, node) -> Var | list[Var]: — read inputs via operands(ctx.values_map, node, [...]), emit mb.* ops, and name the final op for each output with name=node.output[i] (so Core ML's predicted output keys match ONNX names). Never pass name=None to an intermediate op — omit the kwarg.
  3. Add the op key to that module's REGISTRY dict. Keys are unique across modules (a duplicate raises at import).
  4. Find the exact MIL op + parameter names in ../coremltools/coremltools/converters/mil/mil/ops/defs/ — do not guess.
  5. Add a parity test in tests/test_ops_<family>.py parametrized over fmt in ["mlpackage", "mlmodel"].

Helpers in _lowering/_common.py: binary(mb_op), unary(mb_op), const_array(ctx, node, idx), get_attr, operands.

Testing

.venv/bin/python -m pytest tests/ -q          # full suite
.venv/bin/python -m pytest tests/test_ops_conv.py -q

Read the full file on GitHub · 66 lines

Changes

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.

  1. 2mo ago First seen · 66 lines · 831 tokens per session scan A e9173382e2b8

Subscribe to this mod's changes

onnx2coreml AGENTS.md is an instructions file published in the GitHub repository devin-lai/onnx2coreml (149 stars, last pushed 4mo ago), licensed BSD-3-Clause. It adds 831 tokens to every session, about $0.0033 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.

Apple SiliconData and AIMobilecoremlml-conversiononnxonnx-to-coreml
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