SKILL·4CDF32

hivemind-graph

activeloopai
更新于 8 days ago
1,547
97
1,547
在 GitHub 上查看
automationdesign

关于

This skill lets developers query a live AST-derived code graph to answer structural questions about their codebase, like finding callers, imports, or definitions. It automatically rebuilds from the repository and is accessed via a local Deeplake mount, requiring no manual build steps. Use it when you need to understand code architecture, dependencies, or relationships between functions and classes.

快速安装

Claude Code

推荐
主要方式
npx skills add activeloopai/hivemind -a claude-code
插件命令备选方式
/plugin add https://github.com/activeloopai/hivemind
Git 克隆备选方式
git clone https://github.com/activeloopai/hivemind.git ~/.claude/skills/hivemind-graph

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

Hivemind Code Graph

A deterministic, AST-derived map of the current repository — every function, class, method, interface, type, enum, const, and module, plus the edges between them (calls, imports, extends, implements, method_of). It is queried as synthesized files under the Deeplake mount; there are no real files on disk and no network call in the read path.

The graph builds and refreshes automatically (on Stop / SessionEnd, gated by a rate limit + git diff). You never run a build command — just read it.

Use it as a fast INDEX to locate the few files/symbols that matter, then open them with Read to answer. It is not a substitute for the source.

When to use this skill

Activate when the user asks a structural / relational question about the code:

  • "What calls pushSnapshot?" / "Who uses this function?"
  • "What does deeplake-pull.ts import?" / "What depends on X?"
  • "Where is GraphSnapshot defined?" / "Find the function that handles Y."
  • "What are the main subsystems / the architecture here?"
  • "If I change this signature, what's affected?" → use impact/<symbol> (transitive blast radius)

When NOT to use this skill

  • Reading the body of a symbol you already located → use Read on the real source file. The graph gives location + relationships, not full source.
  • Code that isn't committed/built yet — the graph can lag uncommitted edits. If a file's mtime is newer than the build timestamp, read the live source.
  • Languages outside TypeScript, JavaScript, and Python (Go, Rust, …) — the extractor covers those three, with cross-file calls/imports resolved for named imports. For anything else, fall back to grep/read.

Path cheat sheet

cat ~/.deeplake/memory/graph/index.md
#   Overview: node/edge counts, kind breakdown, top files by node count.

cat ~/.deeplake/memory/graph/query/<pattern>   # START HERE (the 2-in-1)
#   Search + expand the top matches with their 1-hop neighbors (callers,
#   callees, imports, heritage). Multi-token AND: query/<a>+<b>.

cat ~/.deeplake/memory/graph/find/<pattern>
#   Case-insensitive substring search on node id + label (max 50 hits).
#   Prints numbered handles [1] [2] ... saved for this worktree.

cat ~/.deeplake/memory/graph/show/<handle-or-pattern>
#   <handle>: a digit from a prior find/ (e.g. 3).
#   <pattern>: a substring → unique node detail, or a candidate list.
#   Output: the node + its 1-hop neighbors grouped by edge relation.

cat ~/.deeplake/memory/graph/neighborhood/<file>
#   Every symbol in a file + its cross-file neighbors (callers/callees/imports).

cat ~/.deeplake/memory/graph/impact/<pattern>
#   Transitive dependents — the blast radius of changing a symbol.

cat ~/.deeplake/memory/graph/path/<from>/<to>
#   Shortest dependency path between two symbol patterns (trace a flow across files).

cat ~/.deeplake/memory/graph/layers      # architectural layers / subsystems
cat ~/.deeplake/memory/graph/tour        # deterministic guided walkthrough

Workflow

  1. Broad? Start at index.md to see subsystems and the biggest files.
  2. Looking for a symbol? find/<name> (or query/<name>) → pick the handle.
  3. Want relationships? show/<handle> / neighborhood/<file> → callers/callees, imports.
  4. Tracing a flow? path/<from>/<to>. Change impact? impact/<symbol>.
  5. Need the actual code? Take the source_file:line and Read it — don't answer from the graph alone.

Anti-patterns (read these)

  • "Incoming (0)" does NOT mean dead code. Cross-file calls are resolved for named imports (TS/JS/Python), but instance-method dispatch (obj.method()), dynamic calls, and nested/inner functions are NOT — a zero-incoming symbol may still be reached via one of those. Confirm in the source before calling it unused.
  • The graph can be stale. It rebuilds at most once per rate-limit window. The SessionStart inject prints the build age; if it's old or you've just edited a file, prefer the live source for that file.
  • Don't try to build it. There is no user-facing build step in normal use; the hooks handle it. Just read the mount.
  • find/ is lexical, not semantic. It matches substrings, not meaning — find/auth won't surface login/credentials unless those strings appear in the id/label. Try multiple keywords if the first misses.

GitHub 仓库

activeloopai/hivemind
路径: harnesses/codex/skills/hivemind-graph
0
aiai-agentsai-memoryanthropicartificial-intelligenceclaude
FAQ

常见问题

什么是 hivemind-graph Skill?

hivemind-graph 是一个 Claude Skill,作者为 activeloopai。Skill 将 Claude 按需加载的说明和资源打包,让 Claude 无需额外提示即可执行与 hivemind-graph 相关的任务。

如何安装 hivemind-graph?

使用本页的安装命令:将 hivemind-graph 作为插件添加到 Claude Code,或将其仓库克隆到 skills 目录,然后重启 Claude 以加载该 Skill。

hivemind-graph 属于哪个分类?

hivemind-graph 属于元分类。

hivemind-graph 可以免费使用吗?

可以。hivemind-graph 已收录在 AIMCP,可免费安装。

相关推荐技能

content-collections

Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。

查看技能
polymarket

这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。

查看技能
creating-opencode-plugins

该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。

查看技能
sglang

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能