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hivemind-graph

Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/. Use when the user asks structural questions about the codebase — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).

76

Quality

95%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is hivemind-graph in activeloopai/hivemind

SKILL.md
Quality
Evals
Security

Quality

Content

100%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 content is lean, fully executable, and well-structured: concrete commands, a clear sequenced workflow, and an anti-patterns section that supplies the validation guidance a read-only querying skill needs. No bundle files exist, and none are needed.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence; it explains only the custom system specifics Claude could not know (the Deeplake mount, edge types, build behavior) and every line earns its place with no padded concept explanations.

5 / 5

Actionability

The path cheat sheet provides copy-paste-ready `cat` commands covering every common query type (query, find, show, neighborhood, impact, path, layers, tour), fully executable with no pseudocode.

5 / 5

Workflow Clarity

A clear 5-step numbered workflow sequences broad exploration → locate → relationships → flow/impact → read source, and the Anti-patterns section supplies validation guidance (staleness checks, "Incoming (0) is not dead code") appropriate for this read-only skill.

5 / 5

Progressive Disclosure

A single well-organized file with clear section headers and no nested references; all query paths are documented inline at one level, which is appropriate given there are no bundle files to split out.

5 / 5

Total

20

/

20

Passed

Description

91%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, third-person description that clearly states both capability and trigger conditions with natural user phrasings. It is highly specific to its niche, with only minor overlap risk against generic code-search skills.

DimensionReasoningScore

Specificity

The description names one concrete action ("Query the local code graph") and lists several specific queryable objects (functions, classes, calls, imports), which lists several specific capabilities but is essentially a single action with minor coverage gaps rather than multiple distinct actions.

4 / 5

Completeness

It explicitly answers both what ("Query the local code graph...") and when ("Use when the user asks structural questions about the codebase") with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

It includes four natural quoted phrasings a user would actually say ("what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?"), giving comprehensive coverage of natural trigger terms.

5 / 5

Distinctiveness Conflict Risk

It carves a clear niche (AST-derived code graph via Deeplake mount for structural questions) with distinct triggers, but classic phrasings like "where is Z defined" overlap somewhat with general grep/code-search skills, so minor overlap risk remains.

4 / 5

Total

18

/

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
activeloopai/hivemind
Reviewed

Table of Contents

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