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fineenvs

Agents & models

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Updated 10-29 04:37

About this skill

FineEnvs operator

FineEnvs is an Apache-2.0 Python repository audited at 5e0b46e96bfd20105976e50a06a3b79c9c62f076 (2026-10-12). Its project-local READMEs and configs remain authoritative; historical runs, moving main dependencies, paid services, and known runtime gaps are not reproducibility guarantees.

When to use this skill

  • Inspect or pin a FineEnvs checkout and select one environment/project path.
  • Run a no-backend Wordle smoke or another explicitly approved local fixture check.
  • Read reward, grader, coverage, data-license, or framework-contract evidence.
  • Author a new environment using the bundled domain and framework-generator contracts.

Do not use this skill as a generic training, serving, Ray, sandbox, Hugging Face, provider, or cloud-operations guide. Use the owning skill for those operations; consult the audited reference here to identify their prerequisites and side effects.

Instructions

  1. Preflight offline. From the repo root run python3 .agent-skills/fineenvs/scripts/preflight.py --json; from an installed skill root run python3 "$SKILLS_ROOT/fineenvs/scripts/preflight.py" --json. For a checkout, add --repo PATH --expect-commit 5e0b46e96bfd20105976e50a06a3b79c9c62f076. Treat runtime_verified:false as inspection only: this helper does not import frameworks, read secrets, query HTTP/GPU state, create sandboxes, or run evaluations. Optional framework markers are informational; do not require all dialects at once.
  2. Pin and read. Verify the supplied checkout commit, then read its project-local README and relevant pyproject.toml, REPRODUCE.md, VALIDATION.md, or eval/README.md. Read references/upstream.md for the audited matrix, source links, framework contracts, reward caveats, licensing, branch requirements, and cloud effects.
  3. Classify before acting. Keep inventory, source inspection, and no-backend local fixtures separate from credentials, downloads, service startup, provider inference, E2B/Daytona or HF sandboxes, paid Spaces, GPU Jobs, Hub/bucket writes, and deployment. Obtain explicit approval for the latter and state provider, model, hardware, data volume, output path, and spend.
  4. Choose the smallest path. Wordle Verifiers is the low-cost local route: cd 00-environments-101/envs/wordle/verifiers && uv sync && uv run python rollout.py. Keep repository-root .env handling as documented; never invent per-project secret files.
  5. Preserve evidence. Report the pinned commit, project path, resolved versions or commits, split, model/checkpoint, command, endpoint, hardware, reward/scorer settings, coverage, and output directory. A smoke proves execution/checkpoint reload, not learning or quality.
  6. Author safely. Keep one framework-independent domain module, gather goal/action/state/ reward/backend/termination/path requirements, then use the bundled authoring or framework generator skill. Finish with the selected framework's import, discovery, and rollout checks; do not start training as part of authoring.

Examples

# Offline host/check-out inspection; no upstream process is launched.
python3 .agent-skills/fineenvs/scripts/preflight.py --json
python3 .agent-skills/fineenvs/scripts/preflight.py --json \
  --repo /path/to/FineEnvs --expect-commit 5e0b46e96bfd20105976e50a06a3b79c9c62f076

# Lowest-cost local smoke after reading the pinned project instructions.
cd 00-environments-101/envs/wordle/verifiers
uv sync && uv run python rollout.py

# Offline LaTeX OCR fixture path (the --real-data variant is not offline).
uv run --project 01-latex-ocr/envs/latex_ocr --extra dev \
  pytest 01-latex-ocr/envs/latex_ocr/tests -q
uv run --project 01-latex-ocr/envs/latex_ocr latex-ocr-smoke

Best practices

  • Read project-local instructions at the pinned revision; the root quickstart is not a universal contract.
  • Keep local endpoints on localhost and use documented variables; never probe production Spaces.
  • Treat historical tables as qualified evidence, not current benchmark claims; report coverage with pass@1.
  • Preserve Apache-2.0 notices and PortSim CC BY-SA 4.0/ODbL attribution obligations.
  • Do not authorize cloud spend, data licensing, credentials, or external side effects on a user's behalf.

References