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pufferlib

Version-aware guidance for PufferLib reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review. Covers the native 5.0 build and environment API, published 3.0.0 Gymnasium/PettingZoo adaptation, and a pinned historical 4.0 profile.

68

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exceptionally well-engineered body: version-aware decision tables, copy-paste executable commands with pinned hashes and commits, gated workflows with validation wired into every risky step, and clean one-level-deep progressive disclosure backed by a real, complete bundle. The only weakness is minor: a few passages re-explain concepts Claude already knows and provenance caveats repeat.

DimensionReasoningScore

Conciseness

The body is dense and telegraphic, dominated by version-pinned facts Claude cannot know (revision hashes, sdist sha256, pinned uv commands, per-version API and CLI differences), and organizes time-sensitive material in a Profile table and a "Dated upstream sources" section. It is not a 5 because a few passages explain known concepts (the "terminated is an MDP terminal; truncated is an external cutoff" tutorial, the torch.load unpickling warning) and the "illustrative and were not executed" provenance caveat is repeated several times; not a 3 because these trimmable instances are minor against ~370 tightly-packed lines.

4 / 5

Actionability

Guidance is copy-paste executable throughout: bundled CLI invocations with concrete flags ("--steps 64 --episodes 8 --seed 42"), pinned install commands ("uv add --exact --no-sync \"pufferlib==3.0.0\"", git URL with full commit hash), exact Python API shapes (emulation wrappers, vector.make, pufferl.PuffeRL), and a complete report→plan→validate pipeline including a working python -c JSON extraction. Specific examples cover the common cases for all three profiles, matching the 5 anchor.

5 / 5

Workflow Clarity

Workflows are explicitly sequenced with validation checkpoints: a 10-item numbered safe-defaults checklist, "First local checks" before any installation, environment workflow steps 1-3, and gated transitions like "Move to Multiprocessing only after serial traces pass" and "Hash checkpoint bytes before trusted, sandboxed loading". Validation is wired into every risky step (env_contract_validator, validate_plan before handoff, inspect_checkpoint with --expected-sha256), matching the 5 anchor's explicit validation and checklists.

5 / 5

Progressive Disclosure

SKILL.md is a genuine overview that splits detail into six one-level-deep references (all verified to exist), each signaled at its decision point ("Read references/training.md before any installation or build") and again in a documented listing with per-file descriptions; every referenced script exists and is itemized. Reference cross-links (e.g. to native-5.md) are same-level siblings rather than nested chains, so navigation stays one hop deep — matching the 5 anchor.

5 / 5

Total

19

/

20

Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, highly specific description that names the exact library, its capability surface, and three version profiles, with good natural trigger terms and virtually no conflict risk. Its main weakness is the missing 'Use when...' clause: the trigger half of the description is entirely absent, capping completeness at 3.

Suggestions

Append an explicit trigger clause, e.g. "Use when working with PufferLib, PuffeRL, or PufferLib-based RL environments, vectorization, policies, training, evaluation, or checkpoints" — this lifts completeness from 3 to 5.

Add natural synonyms such as "RL" or "reinforcement learning environments" so users who say 'RL library' rather than the full term also match.

Mention the logging/benchmarking coverage (e.g. "W&B logging and safe benchmarking") so the description reflects the body's full capability surface.

DimensionReasoningScore

Specificity

The description enumerates six concrete capability areas — "reinforcement-learning environments, vectorization, policies, PuffeRL training, evaluation, and safe checkpoint review" — and scopes them across three explicit profiles ("native 5.0 build", "published 3.0.0 Gymnasium/PettingZoo adaptation", "pinned historical 4.0 profile"), matching the 'several specific actions with minor gaps' anchor. It is not a 5 because capabilities are topic nouns under the generic verb 'guidance for' rather than concrete action verbs, and body-covered areas like logging and benchmarking are absent; it exceeds a 3 because coverage goes well beyond 1-2 actions.

4 / 5

Completeness

The 'what' is clear and specific (version-aware guidance across environments, vectorization, policies, training, evaluation, checkpoints for three pinned profiles), but there is no 'Use when...' clause or equivalent explicit trigger guidance anywhere in the description, which caps completeness at 3 per the judging guidelines. It cannot be a 4 because the 'when' is not even weakly implied, and it is not a 2 because the 'what' half is fully present and concrete.

3 / 5

Trigger Term Quality

It contains the natural terms a PufferLib user would say: "PufferLib", "reinforcement-learning", "vectorization", "policies", "training", "evaluation", "checkpoint", plus "Gymnasium" and "PettingZoo". Not a 5 because common synonyms such as "RL" and related phrasings like "train an RL agent" are missing; clearly above a 3 since keyword coverage is domain-specific and broad rather than partial.

4 / 5

Distinctiveness Conflict Risk

"PufferLib" names a specific niche library, and the version-scoping ("native 5.0", "pufferlib==3.0.0", "pinned 4.0") plus library-specific terms like "PuffeRL" make false triggers on other RL/training skills very unlikely — a clear niche with distinct triggers and minimal conflict risk, matching the 5 anchor exactly.

5 / 5

Total

16

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/pufferlib/SKILL.md

Reviewed

Table of Contents

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