agent-pagerank-analyzer

Agent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer

9.44x

Quality

11%

Does it follow best practices?

Impact

85%

9.44x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./.agents/skills/agent-pagerank-analyzer/SKILL.md

Quality

Discovery

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

This is an extremely weak skill description that essentially only names the skill and provides an invocation command. It fails to describe any concrete capabilities, lacks natural trigger terms, and provides no guidance on when Claude should select this skill. It is barely functional as a description.

Suggestions

Add concrete actions the skill performs, e.g., 'Analyzes link structures and computes PageRank scores for web pages or graph nodes, identifies most influential nodes, and visualizes ranking distributions.'

Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about page ranking, link analysis, graph centrality, SEO ranking, web page importance, or network influence analysis.'

Remove the invocation instruction ('invoke with $agent-pagerank-analyzer') from the description field, as this is operational detail that doesn't help Claude decide when to select the skill.

Dimension	Reasoning	Score
Specificity	The description provides no concrete actions whatsoever. 'Agent skill for pagerank-analyzer' is extremely vague and does not describe what the skill actually does beyond naming itself.	1 / 3
Completeness	The description fails to answer both 'what does this do' and 'when should Claude use it'. It only provides an invocation command with no explanation of capabilities or usage triggers.	1 / 3
Trigger Term Quality	The only potentially relevant keyword is 'pagerank' which is a technical term. There are no natural user-facing trigger terms like 'rank pages', 'link analysis', 'SEO', 'graph analysis', or similar terms a user would naturally say.	1 / 3
Distinctiveness Conflict Risk	The term 'pagerank-analyzer' is at least somewhat distinctive as a niche topic, which reduces conflict risk with other skills. However, the lack of detail means it's unclear what exactly distinguishes it from other potential analysis or graph-related skills.	2 / 3
	Total	5 / 12 Passed

Implementation

14%

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

This skill is excessively verbose, containing large amounts of descriptive content that Claude already knows (graph theory concepts, ML techniques, optimization strategies) without providing proportional actionable guidance. The code examples are partially useful but often rely on undefined helper functions, and the workflow sections lack concrete steps and validation checkpoints. The entire document should be dramatically condensed to focus on the specific MCP tool interfaces and their parameters.

Suggestions

Reduce content by 70%+: remove all descriptive bullet lists about graph concepts (community detection, network dynamics, application domains) and focus exclusively on MCP tool usage with concrete parameters and expected outputs.

Make code examples fully executable by removing undefined helper functions like `identifyInfluencers()` and `extractTopRecommendations()`, or provide their implementations.

Add validation checkpoints to workflows: after PageRank computation, verify convergence, check score distributions, and handle error cases (e.g., disconnected graphs, singular matrices).

Split content into a concise SKILL.md overview with MCP tool quick-reference, and move detailed integration patterns and application examples into separate referenced files.

Dimension	Reasoning	Score
Conciseness	Extremely verbose with extensive sections that describe concepts Claude already knows (community detection, graph ML, performance optimization techniques). Bullet-point lists of abstract capabilities like 'Spectral Clustering: Use spectral methods for community identification' add no actionable value. The content is heavily padded with domain descriptions that waste tokens.	1 / 3
Actionability	Some code examples show concrete MCP tool invocations with parameters, which is useful. However, many examples are pseudocode-like (e.g., `extractTopRecommendations`, `identifyInfluencers`, `load_graph_partition` are undefined helper functions), and large sections are purely descriptive bullet lists with no executable guidance.	2 / 3
Workflow Clarity	The 'Example Workflows' section lists high-level steps like 'Build social network graph from user interactions' and 'Compute PageRank scores' without any concrete commands, validation checkpoints, or error recovery. Multi-step processes are described abstractly with no verification steps between stages.	1 / 3
Progressive Disclosure	The content is a monolithic wall of text spanning many sections that could be split into separate reference files. There are no references to external files for detailed content. Everything from basic usage to advanced algorithms to application domains is inlined in one massive document with no navigation structure.	1 / 3
	Total	5 / 12 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 — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: ruvnet/ruflo
Commit: 0f7c750

Reviewed: 3 days ago

Table of Contents

Discovery Implementation Validation

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