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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's the architecture / which subsystems exist?", "what's the impact of changing this?". The graph is an AST-derived map of the repo, queried as files (no build needed — it rebuilds automatically).

80

Quality

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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 body is a model of a tight, self-contained lookup skill: executable commands, a decision-driven workflow, and an anti-patterns section that doubles as validation guidance. No bundle files are present or required.

DimensionReasoningScore

Conciseness

Lean and dense throughout — the overview, cheat sheet, and anti-patterns each carry actionable signal with no explanations of concepts Claude already knows; every token earns its place.

5 / 5

Actionability

The path cheat sheet gives copy-paste-ready commands (cat ~/.deeplake/memory/graph/query/<pattern>, impact/<symbol>, path/<from>/<to>) with concrete expected output comments covering the common query cases.

5 / 5

Workflow Clarity

The numbered Workflow gives a clear decision-driven sequence (broad→find→show→path/impact→Read source), and the Anti-patterns section supplies validation checkpoints (confirm "Incoming (0)" in source, check build age before trusting stale graph) for this read-only skill.

5 / 5

Progressive Disclosure

No bundle files exist and none are needed; the body is self-contained, well-split into clearly-headed sections (When to use / NOT to use / cheat sheet / Workflow / Anti-patterns) with easy header-based navigation and no nested references.

5 / 5

Total

20

/

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 exemplary: third-person voice, concrete capabilities, natural trigger phrases, and a distinct niche. It answers what and when with no padding or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — querying "functions, classes, calls, imports" plus transitive "impact of changing this" — giving comprehensive coverage of the graph's query surface rather than vague abstraction.

5 / 5

Completeness

Explicitly answers both what ("Query the local code graph ... AST-derived map of the repo") and when ("Use when the user asks structural questions about the codebase") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Includes the exact natural phrases users say — "what calls X?", "what does Y import?", "where is Z defined?", "what's the architecture / which subsystems exist?", "what's the impact of changing this?" — covering synonyms and variations comprehensively.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche — structural/relational code questions answered via a specific Deeplake graph mount — with distinct triggers and minimal overlap risk against generic code-search or read skills.

5 / 5

Total

20

/

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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