rlenv-audit 0.3.0

env_audit — a skill-based auditing system for Prime Intellect `verifiers` RL environments

env_audit

A skill-based auditing system for RL environments. Point an agent (Claude Code / Codex) at a verifiers environment from the Prime Intellect Hub and it runs six checks and produces a scorecard — before you spend GPU hours training on a broken reward.

RL environments are treated like training data, but nobody tests them first. A broken reward function doesn't crash — it silently teaches the policy garbage. env_audit catches that.

Why skills, not scripts

The six checks are judgment-heavy, non-deterministic evaluations — "does this reward agree with a competent grader?", "is the system prompt missing something?", "does this dataset overlap a benchmark?". Those are done well by an agent, not a hard-coded script. So each check is a skill file (skills/<check>/SKILL.md) that the agent reads and executes with its own reasoning, leaning on a small layer of deterministic tools (rlenv-audit ...) for the exact parts: loading the env, calling the reward function, running rollouts, rendering the scorecard.

Each check returns a score (0–100), a status, and a written justification.

The six checks

#	Check	Needs	What it does
1	integrity	—	Does it even run and is it shaped right: dataset loads & is well-formed, reward present & callable, follows verifiers conventions, no missing fields / broken imports.
2	problem-statement alignment	(a problem statement)	Given what the user says the env is for, judge whether the dataset + reward + prompt actually test that. N/A if no problem statement is provided.
3	reward design	—	Stress-tests the reward without the policy: the agent writes ~20 synthetic completions (correct / wrong / edge / format perturbations), scores them through the real reward, and checks (a) the reward varies & discriminates sensibly and (b) each reward matches the agent's own judgment of quality.
4	latency	model endpoint	How long rollouts take end to end. Reads the shared cached rollouts.
5	rollout quality	model endpoint	Reads actual rollouts and judges whether the env is set up well in practice — system prompt right, outputs sensible, obvious env-caused failure modes.
6	contamination	—	Infers the domain, picks the public benchmarks for it, and checks whether dataset instances match/near-match benchmark instances.

Shared rollouts (checks 4 & 5). Both need a model, so env_audit asks once which endpoint/model to use (or "dummy"), runs rollouts once (8 rollouts over ~20 samples, scored + timed, cached), and both checks read that single cache. Checks 1, 2, 3, 6 need no endpoint. No endpoint → 4 & 5 are N/A.

Quickstart

# Install the skills (pick one)
uvx --from git+https://github.com/vivekvkashyap/RLEnv_audit.git rlenv-audit install-skills
pip install git+https://github.com/vivekvkashyap/RLEnv_audit.git && rlenv-audit install-skills

Or as a Claude Code plugin, no terminal needed:

/plugin marketplace add vivekvkashyap/RLEnv_audit
/plugin install env-audit@rlenv-audit

Then point your agent (Claude Code / Codex) at an environment:

"Audit the gsm8k environment."   /   "Audit primeintellect/aime2024 — I'm trying to train a competition-math solver — using my vLLM at http://localhost:8000/v1."

That's it — everything else is self-bootstrapping: on the first audit the skill installs the rlenv-audit tools (if missing) and vf-installs the environment itself. The agent runs the six checks and prints the scorecard:

                               env_audit · gsm8k
┃ check             ┃ status ┃ score ┃ justification                           ┃
│ integrity         │ PASS   │    95 │ loads, reward callable, well-formed     │
│ problem_alignment │ N/A    │     — │ no problem statement provided           │
│ reward_design     │ PASS   │    88 │ discriminates; matches judgment 18/20   │
│ latency           │ N/A    │     — │ no endpoint                             │
│ rollout_quality   │ N/A    │     — │ no endpoint                             │
│ contamination     │ WARN   │    60 │ 3 near-matches with GSM8K test          │
overall: WARN   rating: B (81/100)

From a checkout (development)

pip install -e .                    # the rlenv-audit / env-audit tools
rlenv-audit install-skills          # copy skills/ into ~/.claude/skills
vf-install primeintellect/gsm8k     # install an environment to audit by hand

Most Hub envs require Python 3.11+; verifiers==0.1.14 (pinned) also runs on 3.10 for old-CUDA boxes, where you can install the older example envs. The env must be installed into the same Python environment as rlenv-audit — verifiers loads environments by importing them.

The tools (what the skills call)

rlenv-audit inspect <env> -n 20            # load + introspect -> JSON (reward source, samples, prompt)
rlenv-audit score <env> completions.json   # score agent-written completions through the reward fn
rlenv-audit rollouts <env> --endpoint <url> --model <m> -n 20 -k 8   # run+cache shared rollouts
rlenv-audit rollouts <env> --dummy         # fake rollouts, no endpoint (dry run)
rlenv-audit scorecard results.json         # render the final scorecard

These are deterministic and JSON-in/JSON-out — usable directly, but normally driven by the skills.

What good looks like

REWARD_DESIGN.md is the reference the reward-design and rollout-quality checks judge against — determinism, discrimination, baseline floor, partial credit, bounds, anti-hacking, parser contract, contamination.

Layout

skills/                 the six checks + the env-audit orchestrator (SKILL.md each)
.claude-plugin/         plugin + marketplace manifests (repo doubles as a Claude Code plugin)
rlenv_audit/
  adapters/verifiers.py EnvHandle — the only code that touches verifiers
  tools.py              inspect / score / rollouts / scorecard
  sandbox.py            Docker isolation (for executing risky completions)
  cli.py                the rlenv-audit / env-audit CLI (+ install-skills)
REWARD_DESIGN.md        the design guide the judgment checks cite

Development

pip install -e ".[dev]" && pytest tests/

License

MIT