pufferlib

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.

Quality

80%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

61%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.

The body is action-oriented with strong executable examples and a sound progressive-disclosure structure, but it is weakened by redundant capability/reference restatements and by the absence of explicit validation checkpoints in its batch training workflows.

Suggestions

Collapse the redundant capability pointers: keep either the inline 'For X, read references/X.md' previews or the Resources listing, not both, to remove ~50 lines of repetition.

Add explicit validation checkpoints to the training and environment workflows (e.g. 'Run a smoke test with num_envs=1 and verify the env passes before scaling up'), forming a validate→fix→retry loop to lift workflow_clarity above the cap of 3.

Make the code snippets fully copy-paste ready by defining placeholder variables (obs_dim, num_actions, my_policy) or noting where they come from, so actionability reaches the anchor 5 bar.

Dimension	Reasoning	Score
Conciseness	The core sections are lean with executable code, but the 'When to Use', 'Common Use Cases', and 'Resources' sections substantially restate capability lists and reference pointers already given inline, which could be tightened; sits between anchor 2's noticeable padding and anchor 3's mostly-efficient baseline.	3 / 5
Actionability	Provides concrete, mostly copy-paste-ready CLI commands and Python examples across training, environments, vectorization, policies, and integration, with only minor undefined references (e.g. obs_dim, my_policy) keeping it below anchor 5.	4 / 5
Workflow Clarity	Numbered workflows for training, environment creation, policies, and optimization are well sequenced, but these batch training operations lack explicit validate→fix→retry checkpoints, so per the rubric cap workflow_clarity cannot exceed 3.	3 / 5
Progressive Disclosure	A clear overview with well-signaled, one-level-deep references to five reference files and two scripts (all verified to exist), but the inline reference previews are duplicated by the Resources section, a minor organization gap against the anchor 5 lean-navigation example.	4 / 5
	Total	14 / 20 Passed

Description

100%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.

The description is specific, trigger-rich, and explicitly answers both what the skill does and when to use it, with a clear niche anchored on PufferLib-specific terminology. It matches the strongest reference examples in every dimension.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions across the domain — 'high-performance RL training', 'custom environment development', 'vectorized parallel simulation', 'multi-agent systems', 'implementing PPO training', 'creating PufferEnv environments', 'developing policies with CNNs/LSTMs' — giving comprehensive coverage rather than the 1-2 actions of the anchor 3 example.	5 / 5
Completeness	Explicitly answers both what (the listed capabilities) and when, with concrete trigger phrases 'This skill should be used when working with reinforcement learning tasks including...' and 'Use this skill for implementing PPO training...', mirroring the anchor 5 example.	5 / 5
Trigger Term Quality	Comprehensive natural-term coverage including synonyms ('reinforcement learning'/'RL training') and named frameworks users actually mention ('Gymnasium', 'PettingZoo', 'Atari', 'Procgen', 'PPO', 'CNNs/LSTMs'), matching the anchor 5 breadth.	5 / 5
Distinctiveness Conflict Risk	Tightly scoped to PufferLib-specific concepts (PufferEnv, PuffeRL) plus named framework integrations, giving a clear niche with minimal overlap risk; not the broad anchor 3 'Works with document files' style.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

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: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/pufferlib/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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