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graph-engineering

Teaches an agent graph engineering — both halves. Knowledge graphs (ontology design, entity/relation/event extraction, fusion, GraphRAG/memory serving; distilled and translated from Southeast University's graduate Knowledge Graph course, npubird/KnowledgeGraphCourse, 4.4K stars) and task graphs (agent orchestration — parallel fan-out, verifier separation, the stop rule, human gates). Use when asked to build a knowledge graph, extract entities/relations from text, design an ontology, dedupe/merge entities, add graph memory or GraphRAG to an agent, orchestrate multi-agent workflows as a graph, or LEARN graph engineering — in teaching mode the agent explains each stage with worked examples and generates visual diagram artifacts.

73

Quality

90%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%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-structured instructional skill: a clearly sequenced 9-stage pipeline with explicit validation checkpoints and feedback loops, supported by well-signaled one-level-deep references. It is mostly lean and actionable, with only minor trimming and a few inline-template gaps keeping conciseness and actionability at 4.

DimensionReasoningScore

Conciseness

Mostly lean and assumes Claude's competence (e.g., 'a knowledge graph is a product with a schema, not a pile of triples'), but the Credits section and some repetition between inline stage descriptions and the Reference Files list could be trimmed; not a 5 because not every token earns its place.

4 / 5

Actionability

Concrete, specific directives throughout ('5-15 entity types, 10-30 relation types', 'every relation gets a precise verb name (ACQUIRED, not RELATED_TO)', 'Target ≥90% precision on a 50-item sample') make it actionable; not a 5 because executable prompt templates/code are deferred to references rather than inline, leaving minor gaps.

4 / 5

Workflow Clarity

The 9-stage pipeline is explicitly sequenced with a dedicated quality-gate checkpoint (stage 7: sample, score ≥90% precision, 'fix the prompt/rules not the output, then re-run') plus a '10-document pilot before scaling' gate; matches the anchor-5 example of clear sequence with explicit validation and feedback loops.

5 / 5

Progressive Disclosure

Clear overview with five one-level-deep reference files, each clearly signaled with 'Read when...' guidance and stage associations, all real files present in ./references/; content appropriately split and easy to navigate, matching the anchor-5 example.

5 / 5

Total

18

/

20

Passed

Description

96%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, information-dense description that clearly states capabilities and provides explicit, natural trigger phrases for both knowledge-graph and task-graph work. Minor overlap risk on the orchestration triggers and some non-triggering attribution padding ('4.4K stars') keep it just short of a perfect profile.

DimensionReasoningScore

Specificity

Lists multiple concrete actions across both halves — 'build a knowledge graph, extract entities/relations from text, design an ontology, dedupe/merge entities, add graph memory or GraphRAG to an agent, orchestrate multi-agent workflows' — giving comprehensive coverage; not the 4 anchor because the action list is broad rather than having only minor gaps.

5 / 5

Completeness

Explicitly answers both 'what' (teaches graph engineering across knowledge graphs and task graphs with the listed capabilities) and 'when' via a concrete 'Use when...' clause with multiple trigger phrases; matches the anchor-5 example.

5 / 5

Trigger Term Quality

Natural user-facing phrases with synonyms are present ('build a knowledge graph', 'extract entities/relations', 'design an ontology', 'dedupe/merge entities', 'GraphRAG', 'orchestrate multi-agent workflows', 'LEARN graph engineering'); comprehensive coverage matching the anchor-5 example.

5 / 5

Distinctiveness Conflict Risk

Mostly distinct niche (graph engineering) with clear triggers, but 'orchestrate multi-agent workflows as a graph' creates minor overlap risk with general agent-orchestration skills; not a 5 because of that overlap, not a 3 because the graph framing keeps it clearly scoped.

4 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
Jamie-BitFlight/claude_skills
Reviewed

Table of Contents

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