transformer-attention

Use when reasoning about Transformer self-attention, multi-head attention, positional encoding, masked decoder attention, or why attention replaced recurrence/convolutions in sequence models; not for generic NLP or unrelated attention topics.

Quality

83%

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Passed

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Quality

Content

78%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.

A well-organized reasoning skill with directive conditional rules and a clean structure grounded in a real reference. It is concise and clearly navigable, with only minor over-explanation and the absence of a worked example preventing a top score.

Dimension	Reasoning	Score
Conciseness	The body is efficient, rule-based, and avoids re-teaching Transformer basics, but a few bolded clauses restate reasoning Claude already knows (e.g. 'recurrence forces sequential hidden-state updates and blocks parallelism'); it is above the 'mostly efficient' 3 but short of fully lean 5.	4 / 5
Actionability	Concrete conditional rules ('When X, prefer Y') and a 5-step Approach give directive, specific guidance for reasoning; as an instruction-only skill code absence is not penalized, but no worked example illustrates application, leaving it just below the fully copy-paste-ready 5.	4 / 5
Workflow Clarity	The Approach section sequences identification, diagnosis, trade-off testing, and answer framing clearly; there are no destructive/batch operations requiring validation caps, but no explicit validation checkpoints exist either, so it is a 4 rather than 5.	4 / 5
Progressive Disclosure	A compact, well-sectioned overview (When to use, Core decision rules, Approach, References, Known gaps) with a single one-level-deep reference to a real file (references/original-transformer-paper.md), matching the 'clear overview with well-signaled one-level-deep references' anchor.	5 / 5
	Total	17 / 20 Passed

Description

87%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 focused, well-scoped description with explicit triggers and exclusions. It answers both what and when concretely, with only minor gaps in action breadth and trigger-term variation.

Dimension	Reasoning	Score
Specificity	Names the Transformer-attention domain and lists several concrete sub-topics ('self-attention, multi-head attention, positional encoding, masked decoder attention') as the object of reasoning, matching the 'several specific actions; minor gaps' anchor; not a 5 because 'reasoning about' is a single activity type rather than a breadth of distinct actions.	4 / 5
Completeness	Explicit 'Use when reasoning about...' trigger plus concrete topic list answers both 'when' and 'what', and adds explicit 'not for' exclusions, matching the anchor for clearly answering both with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes natural technical phrases users say about this topic (Transformer self-attention, multi-head attention, positional encoding, masked decoder attention) plus exclusion cues; a few natural variations (e.g. 'attention mechanism', 'query/key/value') are absent, so it sits at 'good keyword coverage; a few missing' rather than 5.	4 / 5
Distinctiveness Conflict Risk	A clear niche (original Transformer attention reasoning) with explicit negative scope ('not for generic NLP or unrelated attention topics') yields minimal overlap with other skills.	5 / 5
	Total	18 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	15 / 16 Passed

Repository: VectifyAI/OpenKB
Path: examples/skills/transformer-attention/SKILL.md
Commit: ff54396

Reviewed: 6 days ago

Table of Contents

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