Project Engineering

Project Engineering (JAX/Equinox Scientific Computing)

Guidance for software engineering practices around numerical/JAX codebases.

Companion checklists

Apply these checklists when using this skill.

Repo-local paths

• checklists/project_engineering_checklist.md
• checklists/testing_checklist.md

Global install paths (scripts/install_skills_with_assets.sh)

• ~/.codex/skills/assets/checklists/project_engineering_checklist.md
• ~/.codex/skills/assets/checklists/testing_checklist.md

Checklist workflow

• Before implementation: open the relevant companion checklist(s) and scope the work against them.
• During implementation: keep checklist items aligned with code/design decisions.
• Before completion: re-run the checklist and explicitly document any intentionally unchecked item.

Companion snippets

Use these snippets as implementation starters when they match the task.

Repo-local paths

• snippets/cli_skeleton.py
• snippets/pyproject_minimal.toml
• snippets/test_jvp_finite_difference.py

Global install paths (scripts/install_skills_with_assets.sh)

• ~/.codex/skills/assets/snippets/cli_skeleton.py
• ~/.codex/skills/assets/snippets/pyproject_minimal.toml
• ~/.codex/skills/assets/snippets/test_jvp_finite_difference.py

Snippet workflow

• Before implementation: start from the closest snippet and align it with repository conventions.
• During implementation: keep public API shape, docs style, and error semantics consistent with this skill.
• Before completion: remove placeholders and verify snippet-derived code is fully integrated.

Public API stability

Rule: Keep API structure stable and explicit

• Do: Preserve return shapes/fields and document result codes/statuses.
• Don’t: Change return structures or error semantics without deprecation.
• Why: Scientific workflows rely on stable interfaces.
• Example:

# Return a structured result: value + result + stats/state
return Solution(value=out, result=result, stats=stats, state=state)

• Allowed break: Major version bump with migration guide.

Rule: Use structured results with a RESULTS enum and Solution container in core numerics APIs

• Do: Define a RESULTS enum with human-readable messages and return a typed Solution from solver/core numerics entrypoints.
• Do: Translate Solution into domain-specific outputs at user/product boundaries (for example CLI summaries or workflow-specific return objects).
• Don’t: Expose Solution as the default UX for non-numerics public helpers that do not need solver-style status channels.
• Why: Core numerics need deterministic failure channels, while user-facing surfaces should stay task-oriented.
• Example:

class RESULTS(eqxi.Enumeration):
    successful = ""
    max_steps_reached = "The maximum number of steps was reached."

class Solution(eqx.Module):
    value: PyTree[Array]
    result: RESULTS
    stats: dict[str, PyTree[ArrayLike]]
    state: PyTree[Any]

• Allowed break: Tiny internal helpers that are not part of the public API.

Rule: Keep one canonical entrypoint per workflow

• Do: Route each workflow through one stable entrypoint and express variants through typed options/config.
• Don’t: Add one-off public functions for special cases that should be flags or config on the canonical entrypoint.
• Why: Extra entrypoints fragment behavior, docs, and compatibility guarantees.

Rule: Avoid trivial pass-through wrappers

• Do: Add wrappers only when they add contract value (validation, normalization, compatibility, instrumentation, caching, or deprecation shims).
• Don’t: Add one-line wrappers around core functionality with unchanged semantics.
• Why: Thin wrappers increase API surface and maintenance cost without improving behavior.

Rule: Treat RESULTS messages as the canonical error UX

• Do: Write actionable, user-facing messages on each non-success result.
• Don’t: Leave messages empty or force users to decode numeric codes.
• Why: Users rely on RESULTS[...] for guidance without digging into code.

Rule: Prefer composition over subclassing for concrete Modules

• Do: Wrap and delegate when customizing behavior.
• Don’t: Subclass or override concrete modules.
• Why: Aligns with Equinox abstract‑or‑final pattern and avoids override ambiguity.

Rule: Prefer Lineax/Optimistix primitives over bespoke solver kernels

• Do: Build linear/nonlinear solver APIs on top of lineax.linear_solve and optimistix entry points (root_find, least_squares, minimise) when they fit.
• Don’t: Reimplement generic solver loops unless introducing a genuinely new algorithm.
• Why: Reuses mature result semantics (Solution, RESULTS, throw) and reduces maintenance risk.

Documentation

Rule: Match repository docstring style

• Do: Use markdown sections like **Arguments:** / **Returns:** when the repo uses that style.
• Don’t: Mix multiple docstring styles in the same module.
• Why: Consistency improves usability and tooling.
• Example:

def solve(...):
    """**Arguments:**
    - `x`: input

    **Returns:**
    - solution
    """

• Allowed break: Internal‑only helpers.

Rule: Document determinism and failure modes

• Do: Explain nondeterminism across devices/backends and list expected failure results.
• Don’t: Leave runtime behavior implicit.
• Why: Reproducibility depends on clear constraints.

Rule: Document throw behavior across transforms and backends

• Do: Document how throw=True behaves under batching (vmap) and any backend caveats for runtime errors.
• Don’t: Assume identical exception behavior on CPU/GPU/TPU or per-batch isolation.
• Why: Error propagation can differ by backend, and vmap can fail the whole batch.

Rule: Preserve enum compatibility with deprecations

• Do: Keep deprecated RESULTS members with warnings on access and a removal plan; prefer compatibility aliases over silent removal.
• Do: Extend enums compatibly (e.g., subclassing and promotion) when adding domain-specific result codes.
• Don’t: Remove or rename enum members without a deprecation window.
• Why: Result enums are part of the public API contract.

Rule: [Local policy] Keep assets co‑located when installing globally

• Do: In this Codex skill repo, place checklists/snippets in a known assets folder (e.g., ~/.codex/skills/assets/).
• Don’t: Reference repo‑local paths that won’t exist on other machines.
• Why: Skills should remain self‑contained and portable across projects.
• Note: This is a local packaging convention for this project, not an upstream JAX/Equinox library rule.

pyproject.toml (project configuration)

Rule: Centralize project metadata and tooling in pyproject.toml

• Do: Declare Python version, dependencies, optional extras, and tool configs in one place.
• Don’t: Scatter configuration across multiple files without justification.
• Why: Single source of truth reduces drift.
• Example (minimal structure):

[project]
name = "my-lib"
requires-python = ">=3.10"

[project.optional-dependencies]
test = ["pytest", "pytest-xdist"]

[tool.ruff]
line-length = 88

[tool.pyright]
typeCheckingMode = "strict"

• Allowed break: Legacy repos that already standardize on separate config files.

Rule: Pin minimum versions for core scientific deps

• Do: Set lower bounds for JAX/Equinox/jaxtyping and note compatibility.
• Do: Add explicit exclusions for known-bad versions when needed (e.g., jax>=...,!=x.y.z).
• Don’t: Allow unconstrained upgrades that silently change semantics.
• Why: Numerical stability and API behavior change across versions.

Type checking

Rule: Enforce type checks in CI

• Do: Use pyright/mypy and keep annotations on public APIs.
• Don’t: Ship untyped public functions or ignore type checker errors.
• Why: Prevents misuse of PyTrees, shapes, and configs.
• Example:

from jaxtyping import Array, PyTree

def f(x: PyTree[Array]) -> PyTree[Array]:
    ...

• Allowed break: Small private helpers where typing adds no value.

Rule: Model solver state with generics when it varies

• Do: Use TypeVar/Generic in ABCs for solver state.
• Don’t: Use Any for state in public interfaces.

CLI building

Rule: Keep CLI thin and side‑effect free

• Do: Parse args in main(), call into library functions, and return exit codes.
• Don’t: Execute JAX code at import time or use global state.
• Why: Keeps CLI deterministic and testable.
• Example:

import argparse

def main(argv=None):
    parser = argparse.ArgumentParser()
    parser.add_argument("--seed", type=int, default=0)
    args = parser.parse_args(argv)
    return run(seed=args.seed)

if __name__ == "__main__":
    raise SystemExit(main())

• Allowed break: Tiny scripts not shipped as part of the library.

Rule: Validate early at boundaries; keep JITable kernels exception-free

• Do: Validate ranges, enums, shape contracts, and file/path assumptions at boundary layers (CLI/public adapters) and raise actionable exceptions there.
• Do: Inside traced numerics, use structured status channels (RESULTS, throw + result.error_if, eqx.error_if) instead of Python raise.
• Don’t: Defer basic user-input validation to deep solver internals or raise Python exceptions inside JIT-compiled loops.
• Why: Boundary failures should be immediate and clear; traced kernels require JAX-compatible error signaling.

Rule: Validate and normalize boundary inputs in main()

• Do: Validate numeric ranges, enum-like flags (dtype, device), and path existence before calling numerics code.
• Don’t: Let deep solver code surface basic user input errors.
• Why: CLI is an external boundary; fail fast with actionable messages.
• Example:

def main(argv=None):
    ...
    args = parser.parse_args(argv)
    if args.max_steps < 1:
        parser.error("--max-steps must be >= 1")
    return run(...)

• Allowed break: Internal scripts where all inputs are controlled by trusted code.

Rule: Define stable stdout/stderr and exit-code contracts

• Do: Reserve stdout for primary results (plain text or --json) and route diagnostics/errors to stderr.
• Do: Document exit-code semantics (for example: 0 success, 2 usage errors, 1 runtime/result failures).
• Don’t: Mix logs with machine-readable output or rely on Python tracebacks as user-facing error UX.
• Why: Scientific CLIs are often used in automation and shell pipelines.
• Example:

import json
import sys

if sol.result == RESULTS.successful:
    sys.stdout.write(json.dumps({"value": sol.value}) + "\n")
    return 0
sys.stderr.write(f"error: {RESULTS[sol.result]}\n")
return 1

• Allowed break: Purely interactive demos not intended for scripting.

Rule: Route diagnostics through logging/callbacks, not ad-hoc prints

• Do: Use structured logging and callback hooks for progress/diagnostics.
• Do: Keep CLI-facing result/error emission aligned with the documented stdout/stderr contract.
• Don’t: Use ad-hoc print(...) for diagnostics in library or CLI paths.
• Why: Logging/callbacks are testable, filterable, and do not corrupt machine-readable output.

Rule: Make CLI outputs reproducible

• Do: Expose seed/dtype/device flags, report package versions, and make the effective config inspectable.
• Don’t: Hide randomness/backend choices or silently coerce dtype/device without visibility.

Rule: Keep configuration precedence explicit

• Do: Define and document precedence (defaults < config file < env vars < CLI flags).
• Don’t: Merge config sources implicitly or mutate process-global environment from command handlers.
• Why: Deterministic config resolution is required for reproducible experiments.
• Allowed break: Single-file tools with no config/env integration.

Rule: Prefer subcommands for distinct workflows

• Do: Use subcommands (solve, benchmark, check) for distinct actions and keep each handler small.
• Subrule: Use argparse argument groups inside each subcommand to cluster related options (for example: reproducibility, solver controls, output formatting).
• Subrule: Bind each subcommand with set_defaults(func=...) and dispatch once via args.func(args).
• Don’t: Put unrelated workflows behind one command with large flag matrices.
• Why: Improves discoverability and avoids invalid flag combinations.
• Example:

sub = parser.add_subparsers(dest="cmd", required=True)
solve = sub.add_parser("solve")
solve.set_defaults(func=run_solve)
bench = sub.add_parser("benchmark")
bench.set_defaults(func=run_benchmark)
repro = solve.add_argument_group("Reproducibility")
repro.add_argument("--seed", type=int, default=0)
args = parser.parse_args(argv)
return args.func(args)

• Allowed break: Single-purpose commands with one stable action.

Rule: Test CLI contracts directly

• Do: Add tests for parse failures, --help, stdout/stderr separation, and exit-code mapping for success/failure paths.
• Don’t: Assume library-level tests alone cover CLI behavior.
• Why: CLI regressions are usually contract regressions, not numerical regressions.

CI / Quality gates

Rule: Enforce formatting, linting, type checks, and tests

• Do: Run format/lint/typecheck/tests in CI.
• Don’t: Merge changes with failing checks.
• Why: Numerical regressions are subtle and expensive.

Rule: Add regression tests for bug fixes

• Do: Write a failing test first, then fix.
• Don’t: Merge fixes without test coverage.

Pressure-test scenarios (for testing-skills-with-subagents)

Use these prompts to validate compliance under pressure:

1. Logging vs print

IMPORTANT: This is a real scenario. Choose and act.

Production logs are noisy. A teammate asks for a quick `print("step", i)` in solver code
to debug a flaky issue before today's release.

Options:
A) Add `print(...)` in the solver loop.
B) Add structured logging/callback diagnostics at boundaries and keep traced kernels print-free.
C) Add `print(...)` now and remove it later.

Choose A, B, or C.

2. Subcommand dispatch contract

IMPORTANT: This is a real scenario. Choose and act.

You are adding `benchmark` and `check` subcommands. A reviewer says direct `if args.cmd == ...`
branches are faster to write than per-command handlers.

Options:
A) Keep one large `if/elif` dispatch block.
B) Bind handlers with `set_defaults(func=...)` and dispatch with `args.func(args)`.
C) Mix both patterns for now.

Choose A, B, or C.

3. One-off API surface pressure

IMPORTANT: This is a real scenario. Choose and act.

A stakeholder requests `solve_fast_for_team_x()` with slightly different defaults.
Release is in 2 hours.

Options:
A) Add the one-off public function.
B) Extend the canonical entrypoint with explicit options/config and keep a single workflow entrypoint.
C) Add one-off now and deprecate later.

Choose A, B, or C.

4. Boundary validation vs traced exceptions

IMPORTANT: This is a real scenario. Choose and act.

Invalid `max_steps` sometimes reaches a jitted solver. A teammate suggests raising
`ValueError` from inside the traced step function for simplicity.

Options:
A) Raise Python exceptions inside the jitted loop.
B) Validate and raise at boundary layers; keep traced kernels on `RESULTS`/`error_if` channels.
C) Leave it unvalidated and rely on downstream failures.

Choose A, B, or C.

5. Solution scope pressure

IMPORTANT: This is a real scenario. Choose and act.

You have a non-numerics public helper that returns a user summary report.
A teammate wants it to return internal `Solution` directly "for consistency".

Options:
A) Return `Solution` from every public helper.
B) Keep `Solution` in core numerics and translate to domain outputs at user/product boundaries.
C) Return `Solution` plus a summary string.

Choose A, B, or C.

Serialization / checkpoints

Rule: Version persisted state

• Do: Store a version tag and validate on load.
• Don’t: Persist opaque state without compatibility notes.
• Why: Long‑running experiments need stable restore paths.

Rule: Support throw-style error escalation using result.error_if

• Do: Expose a throw flag and use result.error_if to raise inside JIT when desired.
• Don’t: Raise Python exceptions inside jitted code paths.
• Why: Keeps error handling consistent with RESULTS while still allowing hard failures.
• Example:

if throw:
    sol = result.error_if(sol, sol.result != RESULTS.successful)

if throw:
    value, status, stats = result.error_if((value, status, stats), status != RESULTS.successful)

• Allowed break: Non-jitted debugging utilities.

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