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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).

Quality

88%

Does it follow best practices?

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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

88%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 tight, highly actionable body: real commands with expected outputs, a clear decision workflow, and an unusually good anti-patterns section that doubles as error recovery. The only weaknesses are mild redundancy around the 'index, not source' caveat and a self-contained single-file structure that leaves no room to shed detail as the skill grows.

DimensionReasoningScore

Conciseness

The body is dense and operational — a path cheat sheet, a workflow, and anti-patterns with no filler or explanations of concepts Claude already knows. It sits at anchor 4 rather than 5 because the 'graph is an index, not the source' point is stated three times (intro, 'When NOT to use', workflow step 5) and the intro paragraph restates frontmatter content, minor trimming opportunities.

4 / 5

Actionability

Every command is concrete and copy-paste ready (`cat ~/.deeplake/memory/graph/query/<pattern>`, `show/<handle-or-pattern>`, `path/<from>/<to>`), with placeholders explained (handles come from `find/`, multi-token AND via `query/<a>+<b>`) and expected output described for each. This matches the fully-executable anchor 5.

5 / 5

Workflow Clarity

The Workflow section gives a clear decision-based sequence (broad → index.md, symbol → find/query, relationships → show/neighborhood, flow/impact → path/impact, code → Read), and the Anti-patterns section supplies explicit feedback loops for failure modes: 'Incoming (0)' → confirm in source, stale build → read live source, `find/` misses → try multiple keywords. Anchor 5 fits; anchor 4 would require some checkpoint to be merely implicit.

5 / 5

Progressive Disclosure

The skill has no bundle files (references/scripts/assets are absent) and the ~90-line body is well-sectioned with clear headers, so it is scored against that actual structure. It earns anchor 4 — good structure, content appropriately placed inline, easy navigation — but the under-50-line exception for a 5 does not apply, and sections like the cheat sheet and anti-patterns are dense enough that they could split into references if the skill grows.

4 / 5

Total

18

/

20

Passed

Description

82%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 description with explicit 'what' and 'when' clauses, natural quoted trigger phrases, and a distinct niche. Its main weakness is that it advertises only a single generic action (query) while the skill body exposes a richer command set (find, show, impact, path, layers), slightly understating its capabilities.

Suggestions

Enumerate the concrete query capabilities in the description (e.g., 'find symbols, trace callers/callees, compute change impact, follow dependency paths between symbols') to raise specificity from one generic action to a fuller capability list.

Add one or two common trigger variations such as 'who uses this function' or 'what breaks if I change this' so the trigger coverage better matches how users phrase dependency and impact questions.

DimensionReasoningScore

Specificity

The description names the domain concretely — "Query the local code graph (functions, classes, calls, imports) through the Deeplake mount at memory/graph/" — but offers essentially one action (query); the parenthetical lists graph contents, not additional capabilities. It matches anchor 3 (domain + 1-2 concrete actions, not comprehensive) rather than 4, which expects several distinct specific actions like find/show/impact/path that the body actually supports but the description omits.

3 / 5

Completeness

It explicitly answers both: what ("Query the local code graph (functions, classes, calls, imports) through the Deeplake mount") and when ("Use when the user asks structural questions about the codebase") with concrete quoted trigger phrases. This matches anchor 5; anchor 4 would require the 'when' clause to be less explicit, which it is not. Third-person voice is used correctly.

5 / 5

Trigger Term Quality

Quoted triggers are natural user phrasings — "what calls X?", "what does Y import?", "where is Z defined?", "what is the architecture / which subsystems exist?" — plus "structural questions about the codebase". It falls at anchor 4 (good coverage, a few natural terms missing) rather than 5 because common variations like "who uses this function", "dependencies", "blast radius / impact of a change", or language terms (TypeScript, Python) are absent.

4 / 5

Distinctiveness Conflict Risk

A clear niche — AST-derived code-structure queries against a Deeplake mount — with trigger phrases unlikely to collide with grep/read or general coding skills. The specific mount path and relational-question framing make conflict risk minimal, matching anchor 5.

5 / 5

Total

17

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

activeloopai/hivemind

Path

harnesses/codex/skills/hivemind-graph/SKILL.md

Reviewed

Table of Contents

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