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hivemind-graph

价格未确认★ 1,593 GitHub Stars目录更新于 · 2026年9月2日agent-skill

概览

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).

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

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

Broad? Start at index.md to see subsystems and the biggest files.
Looking for a symbol? find/<name> (or query/<name>) → pick the handle.
Want relationships? show/<handle> / neighborhood/<file> → callers/callees, imports.
Tracing a flow? path/<from>/<to>. Change impact? impact/<symbol>.
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.

查看来源 ↗

文件元数据

name: hivemind-graph
description: 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).
allowed-tools: Read Bash

查看原始文本

---
name: hivemind-graph
description: 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).
allowed-tools: Read Bash
---

# 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

```bash
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.

查看并核实来源

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许可证: Apache-2.0
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已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: activeloopai/hivemind
许可证: Apache-2.0
版本: 1.0.0
最近 GitHub 推送: 2026年9月2日
目录更新于: 2026年9月2日
技能指令路径: harnesses/claude-code/skills/hivemind-graph/SKILL.md @ 916eb4110c23

版本来自目录元数据，使用前请核实来源发布记录。

质量

76/100

强

信任

67/100

仅限沙盒

审计

79/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

更多详情

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      "Audit: 79/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "activeloopai-hivemind-graph (hivemind-graph)",
      "install_command": "npx skills add activeloopai/hivemind --skill hivemind-graph",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "activeloopai-hivemind-graph",
      "task": "Use hivemind-graph in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/activeloopai-hivemind-graph",
    "api": "https://www.openagentskill.com/api/agent/skills/activeloopai-hivemind-graph",
    "audit": "https://www.openagentskill.com/skills/activeloopai-hivemind-graph/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=activeloopai-hivemind-graph&task=Use%20hivemind-graph%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20hivemind-graph%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20hivemind-graph%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/activeloopai-hivemind-graph/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/activeloopai-hivemind-graph"
  }
}

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创作者: activeloopai
来源: activeloopai/hivemind
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 activeloopai，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/activeloopai-hivemind-graph?metric=listed&label=Listed)](https://www.openagentskill.com/skills/activeloopai-hivemind-graph?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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