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dpnp-quickstart

NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can

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価格未確認★ 21 GitHub スター登録情報の更新日 · 2026年10月9日agent-skill

概要

NumPy-compatible array operations optimized for Intel hardware. Use when the user wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp against NumPy. Covers install, device control, fallback patterns, and profiling.

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dpnp quickstart

Purpose

Runs NumPy-style array operations on Intel CPUs and GPUs through dpnp, which mirrors the NumPy API over SYCL device memory. Covers installation, SYCL device selection with dpctl, migrating existing NumPy code, falling back to NumPy for unimplemented APIs, and measuring whether the move actually paid off.

Prefer this over plain NumPy when the arrays are large and the work is math-heavy on Intel hardware. Prefer plain NumPy when the arrays are small — dpnp has dispatch overhead that a small array cannot amortize.

When to Use This Skill

Use this skill when:

The user is migrating or porting NumPy code to Intel CPU or GPU execution.
The user asks whether a NumPy hot path can run on an Intel GPU.
The user needs to check a dpnp install, or which SYCL device will be used.
The user hit a NumPy API that dpnp does not implement.
The user wants to compare dpnp against NumPy.

Use dpnp for:

Large arrays (>10,000 elements)
Math-heavy operations (linear algebra, FFT, reductions)
Intel CPU/GPU acceleration

Do not use this skill — stick with NumPy — for:

Small arrays (<1,000 elements)
I/O operations
APIs not yet implemented in dpnp

Quick Start

Run this first to confirm the environment before changing any user code.

# Conda (officially recommended)
conda install -c https://software.repos.intel.com/python/conda -c conda-forge --override-channels dpnp

# Alternative: pip (may have native dependency issues)
pip install dpnp

# verify installation
python -c "import dpnp; print(dpnp.__version__)"

Then confirm which device the arrays will actually land on:

import dpctl
import dpnp as np

print(dpctl.select_default_device())

x = np.arange(100_000)
print(x.sycl_device)

Report what this prints. Do not claim GPU execution if the output shows a CPU device — the default SYCL device is the GPU only when one is visible.

Implementation Guide

Confirm the environment — install and print the device, as in Quick Start.

Swap the import. dpnp is a drop-in NumPy replacement for the covered API:

# Drop-in NumPy replacement
import dpnp as np

# Create arrays
x = np.array([1, 2, 3, 4])
y = np.arange(1000000)

# Operations work like NumPy
result = np.sum(y)
dot_product = np.dot(x, x)

Port the hot path. Array creation, reductions, and linear algebra keep their NumPy spelling:

import dpnp as np

# Array creation
a = np.zeros((100, 100))
b = np.ones(1000)
c = np.linspace(0, 10, 100)

# Math operations
sum_val = np.sum(a)
mean_val = np.mean(b)
std_val = np.std(c)

# Linear algebra
mat = np.random.randn(100, 100)
result = np.dot(mat, mat.T)

Add fallbacks for uncovered APIs. dpnp implements a subset of NumPy, and coverage is per-parameter, not just per-function. Check with dir(dpnp) or the documentation, and guard the call:

import dpnp
import numpy as np

def safe_unique(x):
    try:
        return dpnp.unique(x)
    except (NotImplementedError, TypeError):
        host_x = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
        return np.unique(host_x)

Convert at API boundaries, not inside loops. Use dpnp.asnumpy() when a downstream library needs a NumPy array. Pandas, scikit-learn, and many NumPy-based libraries usually expect NumPy arrays: use dpnp for the numeric hot path, then convert once with asnumpy() before calling host-oriented libraries. Repeated device-to-host copies inside tight loops can erase acceleration gains.
Constrain the device when the code must be portable. There is no ambient current-device setting and no context manager to enter — placement is an argument at construction. Inspect what is visible with dpctl, then pin allocation with device= or by reusing an existing array's queue, when the same code runs across workstations, containers, and cloud VMs with different SYCL devices:
```
import dpctl
import dpnp

print([d.filter_string for d in dpctl.get_devices()])   # what is actually there

x = dpnp.arange(100_000, device="cpu")                  # pin this array
print(x.sycl_device)

y = dpnp.zeros(x.size, sycl_queue=x.sycl_queue)         # keep the next one beside it
```
Validate, then measure. Compare against NumPy with numpy.testing.assert_allclose() for critical math before making any performance claim.

Performance

No verified benchmark numbers ship with this skill yet. Do not state a speedup that measurement in the user's own environment does not support.

To measure:

Warm up once before timing, to avoid measuring first-run compilation.
Compare against NumPy with the same inputs and dtype.
Profile end-to-end pipelines, including conversions and host-library calls.

Gotchas & Limitations

First run is slow: JIT compilation happens on first execution. Time the second run.
Not all NumPy APIs available: Check compatibility with dir(dpnp) or documentation.
Data transfer cost: Converting between dpnp and NumPy arrays has overhead. Avoid in tight loops.
Small arrays slower: dpnp has dispatch overhead. Use NumPy for small arrays (<1,000 elements).
Parameter-level gaps: a function existing in dpnp does not mean every NumPy keyword argument is accepted — TypeError on an unexpected keyword is the common symptom.
Device assumptions: the default SYCL device is whatever is visible. Code that works on a GPU workstation can land on CPU in CI without erroring.

References

File	Load it when
`references/official-sources.md`	you need to check a claim against upstream documentation — API coverage for a given release, install prerequisites, or which `dpnp` version implements a function

Two questions in this skill should not be answered from memory, and this is where they get answered: is this NumPy API covered (coverage is per keyword argument and changes between releases) and is this package available for the user's platform.

ソースを見る ↗

ファイルのメタデータ

name: dpnp-quickstart
description: >-
  NumPy-compatible array operations optimized for Intel hardware. Use when the user
  wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run
  on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection
  with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp
  against NumPy. Covers install, device control, fallback patterns, and profiling.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. GPU execution requires the Intel GPU driver stack and a SYCL-visible device."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"

元のテキストを表示

---
name: dpnp-quickstart
description: >-
  NumPy-compatible array operations optimized for Intel hardware. Use when the user
  wants to migrate or port NumPy code to dpnp, asks whether a NumPy hot path can run
  on an Intel CPU or GPU, needs to check dpnp installation or SYCL device selection
  with dpctl, hits a NumPy API dpnp does not implement, or wants to compare dpnp
  against NumPy. Covers install, device control, fallback patterns, and profiling.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. GPU execution requires the Intel GPU driver stack and a SYCL-visible device."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"
---

# dpnp quickstart

## Purpose

Runs NumPy-style array operations on Intel CPUs and GPUs through `dpnp`, which
mirrors the NumPy API over SYCL device memory. Covers installation, SYCL device
selection with `dpctl`, migrating existing NumPy code, falling back to NumPy for
unimplemented APIs, and measuring whether the move actually paid off.

Prefer this over plain NumPy when the arrays are large and the work is math-heavy
on Intel hardware. Prefer plain NumPy when the arrays are small — `dpnp` has
dispatch overhead that a small array cannot amortize.

## When to Use This Skill

Use this skill when:

- The user is migrating or porting NumPy code to Intel CPU or GPU execution.
- The user asks whether a NumPy hot path can run on an Intel GPU.
- The user needs to check a `dpnp` install, or which SYCL device will be used.
- The user hit a NumPy API that `dpnp` does not implement.
- The user wants to compare `dpnp` against NumPy.

Use `dpnp` for:

- Large arrays (>10,000 elements)
- Math-heavy operations (linear algebra, FFT, reductions)
- Intel CPU/GPU acceleration

Do **not** use this skill — stick with NumPy — for:

- Small arrays (<1,000 elements)
- I/O operations
- APIs not yet implemented in dpnp

## Quick Start

Run this first to confirm the environment before changing any user code.

```bash
# Conda (officially recommended)
conda install -c https://software.repos.intel.com/python/conda -c conda-forge --override-channels dpnp

# Alternative: pip (may have native dependency issues)
pip install dpnp

# verify installation
python -c "import dpnp; print(dpnp.__version__)"
```

Then confirm which device the arrays will actually land on:

```python
import dpctl
import dpnp as np

print(dpctl.select_default_device())

x = np.arange(100_000)
print(x.sycl_device)
```

Report what this prints. Do not claim GPU execution if the output shows a CPU
device — the default SYCL device is the GPU only when one is visible.

## Implementation Guide

1. **Confirm the environment** — install and print the device, as in Quick Start.
2. **Swap the import.** `dpnp` is a drop-in NumPy replacement for the covered API:

   ```python
   # Drop-in NumPy replacement
   import dpnp as np

   # Create arrays
   x = np.array([1, 2, 3, 4])
   y = np.arange(1000000)

   # Operations work like NumPy
   result = np.sum(y)
   dot_product = np.dot(x, x)
   ```

3. **Port the hot path.** Array creation, reductions, and linear algebra keep
   their NumPy spelling:

   ```python
   import dpnp as np

   # Array creation
   a = np.zeros((100, 100))
   b = np.ones(1000)
   c = np.linspace(0, 10, 100)

   # Math operations
   sum_val = np.sum(a)
   mean_val = np.mean(b)
   std_val = np.std(c)

   # Linear algebra
   mat = np.random.randn(100, 100)
   result = np.dot(mat, mat.T)
   ```

4. **Add fallbacks for uncovered APIs.** `dpnp` implements a subset of NumPy, and
   coverage is per-parameter, not just per-function. Check with `dir(dpnp)` or the
   documentation, and guard the call:

   ```python
   import dpnp
   import numpy as np

   def safe_unique(x):
       try:
           return dpnp.unique(x)
       except (NotImplementedError, TypeError):
           host_x = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
           return np.unique(host_x)
   ```

5. **Convert at API boundaries, not inside loops.** Use `dpnp.asnumpy()` when a
   downstream library needs a NumPy array. Pandas, scikit-learn, and many
   NumPy-based libraries usually expect NumPy arrays: use `dpnp` for the numeric
   hot path, then convert once with `asnumpy()` before calling host-oriented
   libraries. Repeated device-to-host copies inside tight loops can erase
   acceleration gains.
6. **Constrain the device when the code must be portable.** There is no ambient
   current-device setting and no context manager to enter — placement is an argument
   at construction. Inspect what is visible with `dpctl`, then pin allocation with
   `device=` or by reusing an existing array's queue, when the same code runs across
   workstations, containers, and cloud VMs with different SYCL devices:

   ```python
   import dpctl
   import dpnp

   print([d.filter_string for d in dpctl.get_devices()])   # what is actually there

   x = dpnp.arange(100_000, device="cpu")                  # pin this array
   print(x.sycl_device)

   y = dpnp.zeros(x.size, sycl_queue=x.sycl_queue)         # keep the next one beside it
   ```

7. **Validate, then measure.** Compare against NumPy with
   `numpy.testing.assert_allclose()` for critical math before making any
   performance claim.

## Performance

No verified benchmark numbers ship with this skill yet. Do not state a speedup
that measurement in the user's own environment does not support.

To measure:

- Warm up once before timing, to avoid measuring first-run compilation.
- Compare against NumPy with the same inputs and dtype.
- Profile end-to-end pipelines, including conversions and host-library calls.

## Gotchas & Limitations

- **First run is slow**: JIT compilation happens on first execution. Time the second run.
- **Not all NumPy APIs available**: Check compatibility with `dir(dpnp)` or documentation.
- **Data transfer cost**: Converting between dpnp and NumPy arrays has overhead. Avoid in tight loops.
- **Small arrays slower**: dpnp has dispatch overhead. Use NumPy for small arrays (<1,000 elements).
- **Parameter-level gaps**: a function existing in `dpnp` does not mean every
  NumPy keyword argument is accepted — `TypeError` on an unexpected keyword is the
  common symptom.
- **Device assumptions**: the default SYCL device is whatever is visible. Code that
  works on a GPU workstation can land on CPU in CI without erroring.

## References

| File | Load it when |
|---|---|
| [`references/official-sources.md`](references/official-sources.md) | you need to check a claim against upstream documentation — API coverage for a given release, install prerequisites, or which `dpnp` version implements a function |

Two questions in this skill should not be answered from memory, and this is where
they get answered: **is this NumPy API covered** (coverage is per keyword argument
and changes between releases) and **is this package available for the user's
platform**.

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
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI レビュー承認がありません
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, external package install surface
Permission surface: shell or command execution, filesystem or document access

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

登録済み静的チェック済み

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: intel/skills
ライセンス: Apache-2.0
バージョン: 1.0
最終 GitHub プッシュ: 2026年9月29日
登録情報の更新日: 2026年10月9日
手順のパス: skills/dpnp-quickstart/SKILL.md @ 902833d826e7

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

55/100

有望

信頼

60/100

サンドボックス限定

監査

72/100

高リスク

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
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Low GitHub adoption signal
AI レビュー承認がありません
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: shell or command execution, filesystem or document access
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, external package install surface
Permission surface: shell or command execution, filesystem or document access

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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      "license": "Apache-2.0",
      "repository": "https://github.com/intel/skills/tree/main/skills/dpnp-quickstart",
      "install": "npx skills add intel/skills --skill dpnp-quickstart",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "shell or command execution, filesystem or document access",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, external package install surface"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 72,
    "risk_level": "risky",
    "risk_label": "Risky",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
      "Quality score needs review",
      "Permission surface needs review: shell or command execution, filesystem or document access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "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."
  },
  "quality": {
    "score": 55,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "11d since push",
    "risk": "Risky"
  },
  "alternative_skills": [
    {
      "slug": "robium-ai-architect",
      "name": "architect",
      "url": "https://www.openagentskill.com/skills/robium-ai-architect",
      "stars": 21,
      "install_command": "npx skills add robium-ai/robium --skill architect",
      "trust_score": 69,
      "audit_score": 73
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Audit risk risky exceeds max_risk=medium",
    "High-risk permission hints: Shell or command execution",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
  ],
  "agent_contract": {
    "task_input": "Use dpnp-quickstart 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: 68/100 Manual review",
      "Audit: 72/100 Risky",
      "Safety: 44/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-dpnp-quickstart (dpnp-quickstart)",
      "install_command": "npx skills add intel/skills --skill dpnp-quickstart",
      "risk_summary": "Risky; 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": "intel-dpnp-quickstart",
      "task": "Use dpnp-quickstart 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/intel-dpnp-quickstart",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-dpnp-quickstart",
    "audit": "https://www.openagentskill.com/skills/intel-dpnp-quickstart/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-dpnp-quickstart&task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dpnp-quickstart%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-dpnp-quickstart/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-dpnp-quickstart"
  }
}

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: intel
ソース: intel/skills
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 intel 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/intel-dpnp-quickstart?metric=listed&label=Listed)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=trust&label=Trust)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=audit&label=Audit)](https://www.openagentskill.com/skills/intel-dpnp-quickstart/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/intel-dpnp-quickstart?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/intel-dpnp-quickstart?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