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Add, update, or diagnose pytest coverage for RLM behavior using focused fake environments and optional Docker integration tests.

Skill by williamntlam

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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

Add, update, or diagnose pytest coverage for RLM behavior using focused fake environments and optional Docker integration tests.

Use cases: testing, devops

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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  • codeload.github.com · found in install command

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  • ~/.claude/skills/testing

What's inside

Skill name
testing
Description
Add, update, or diagnose pytest coverage for RLM behavior using focused fake environments and optional Docker integration tests.
License
MIT
Instructions
50 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • fake-runtime.md · references/

SKILL.md (first lines)

# Testing

The important regressions here are silent contract failures: an oversized
prompt sent to a model, source data leaking into history, a path escaping its
workspace, or an error that destroys usable diagnostics. Test observable
invariants rather than a model's wording.

Run the smallest relevant module first, then the full suite for changes that
cross runtime boundaries:

```bash
uv run pytest tests/unit/test_<area>.py
uv run pytest tests/integration/test_<area>.py
# Fast RLM product contracts (excludes optional boundaries and eval tooling).
uv run pytest -m "not docker and not eval_support and not harbor"
uv run pytest
uv run ruff check rlm tests
```

For fake runtime tests, read [`references/fake-runtime.md`](references/fake-runtime.md).
Docker tests are separate and require a running daemon:

```bash
uv run pytest -m docker
```

Use `eval_support` for deterministic tests of opt-in judge, case, or benchmark
helpers; never make a live model call from pytest. Use `harbor` for deterministic
Harbor task-layout/verifier checks. Harbor runs themselves are repeated
autonomous-agent integration benchmarks, not pytest tests.

Test placement follows the behavior under test: focused deterministic contracts
go in `tests/unit/`; workflows crossing RLM components, domains, CLI, logging,
or Docker boundaries go in `tests/integration/`; Harbor harness checks go in
`tests/harbor/`; and live-evaluation support stays in `tests/eval_support/`.

Follow [`tests/README.md`](../../tests/README.md): arrange explicit inputs, act
through the public boundary, and assert observable results or artifacts. Use
`make_rlm` for scripted fake-runtime tests; its named `RLMHarness` exposes
`.rlm` and `.client` while preserving tuple unpacking. Do not force pure unit,
Docker, Harbor, and evaluation-support tests into one generic scenario type.

Prefer invariants over exact model-output transcripts.

For planned execution, test both routes with `FakeClient`: a fit record must
consume a bounded leaf before final rendering, and an oversized record must
launch a target-enforced child. Also assert the renderer bindings exclude the
original repo/corpus and that planner fallback remains manifest-scoped.

From williamntlam/recursive-language-model, MIT.

More from williamntlam/recursive-language-model

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Last commit (this folder)
Aug 27, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

testing skill. Agent Mart. https://agentmart.dev/skills/williamntlam-testing (last updated 2026-10-24)