Registry に収録

hivemind-graph

価格未確認★ 1,593 GitHub スター登録情報の更新日 · 2026年9月2日agent-skill

概要

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).

説明全文を読む

ソース文書であり、このサイトへの操作指示ではありません。コマンド実行前に権限を確認してください。

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.

ソースを見る ↗

ファイルのメタデータ

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

元のテキストを表示

---
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.

ソースを確認

価格と実行コスト

Skill の入手: 価格未確認
実行: 実行要件は未確認です。Agent・API・サービス料金を提供元で確認してください。
ライセンス: Apache-2.0
価格未確認: 価格は未確認です。既存のソースとインストールリンクは利用できます。

無料で入手できても実行が無料とは限りません。価格は安全評価ではありません。価格情報を送る →

スキルのソースを記録済み

手順のパスを記録しています。実行テスト、安全保証、互換性認証ではありません。

インストール前にレビュー: 自動インストールを避ける

ライセンス: 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

完全な監査を開く

ツール一覧はメタデータであり、互換性のテスト結果ではありません。プロンプトは提案です。

小さなタスクから始める

1ソースを読み、入力、出力、依存関係、権限を確認します。
2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。

出典と利用上の注意

登録済み

メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ: activeloopai/hivemind
ライセンス: Apache-2.0
バージョン: 1.0.0
最終 GitHub プッシュ: 2026年9月2日
登録情報の更新日: 2026年9月2日
手順のパス: harnesses/claude-code/skills/hivemind-graph/SKILL.md @ 916eb4110c23

登録されたバージョンです。ソースのリリース情報を確認してください。

品質

76/100

強い

信頼

67/100

サンドボックス限定

監査

79/100

要レビュー

完全な監査を開く評価レポートを見る

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: —
成果: —

コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。

Agent 接続

Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。

Install API を開く LLM テキスト形式 Manifest を開く自動解決プラン

詳細情報

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    "creator_verified": false,
    "review_result": "not_recorded",
    "reviewed_at": null,
    "package_fingerprint": null,
    "policy_version": null,
    "notice": "Publication, static checks, AI review, and creator verification are independent facts. None guarantees runtime safety."
  },
  "commerce": {
    "type": "unknown",
    "billing": "unknown",
    "amount": null,
    "currency": null,
    "sourceUrl": null,
    "checkedAt": null,
    "runtime": "unknown",
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    "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).",
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  "trust": {
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      "repoActivity": "1.6K stars, 105 forks",
      "lastPushed": "1mo since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/activeloopai/hivemind/tree/main/harnesses/claude-code/skills/hivemind-graph",
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      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Usable metadata, review docs",
      "agentOutcomes": "No agent outcome data yet"
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      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
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      "setup_required": 0,
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      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
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      "sandbox_required": true,
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      "Permission surface needs review: secrets or environment access, shell or command execution",
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      "Permission surface: secrets or environment access, shell or command execution"
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  "agent_proven": {
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    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
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      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
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      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
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    "signals": [],
    "penalties": [
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  "audit": {
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    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
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      "Permission surface: secrets or environment access, shell or command execution"
    ]
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    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
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  "quality": {
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    "label": "Strong"
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  "supply": {
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    "scenario": "Coding agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
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  "alternative_skills": [],
  "do_not_use_when": [
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    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "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"
  ],
  "agent_contract": {
    "task_input": "Use hivemind-graph in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 75/100 Strong shortlist",
      "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"
  }
}

クリエイター向け

掲載元

Registry により登録

申請可能

この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者: activeloopai
ソース: activeloopai/hivemind
インデックス作成者: OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

このスキルを申請

所有者の申請

このスキル掲載を申請

この Registry により登録掲載は activeloopai に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

共有キット

クリエイター被リンクキット

README にエビデンスバッジを追加

開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![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)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/activeloopai-hivemind-graph?metric=trust&label=Trust)](https://www.openagentskill.com/skills/activeloopai-hivemind-graph?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/activeloopai-hivemind-graph?metric=audit&label=Audit)](https://www.openagentskill.com/skills/activeloopai-hivemind-graph/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/activeloopai-hivemind-graph?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/activeloopai-hivemind-graph?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

バッジをプレビュー監査を開くクリエイターキット

コミュニティシグナル

このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。

概要