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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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Harga belum dikonfirmasi★ 21 Star GitHubDirektori diperbarui · 9 Okt 2026agent-skill

Ringkasan

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.

Lihat sumber ↗

Metadata berkas

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"

Lihat teks asli

---
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**.

Tinjau sumber

Harga dan biaya penggunaan

Dapatkan skill: Harga belum dikonfirmasi
Jalankan: Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
Lisensi: Apache-2.0
Harga belum dikonfirmasi: Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.

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Jalur instruksi telah dicatat. Ini bukan uji eksekusi, jaminan keamanan, atau sertifikasi kompatibilitas.

Tinjau sebelum memasang: Hindari pemasangan otomatis

Lisensi: 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
Persetujuan tinjauan AI belum ada
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

Buka audit lengkap

Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.

Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

TerindeksDiperiksa statis

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: intel/skills
Lisensi: Apache-2.0
Versi: 1.0
Push GitHub terakhir: 29 Sep 2026
Direktori diperbarui: 9 Okt 2026
Jalur instruksi: skills/dpnp-quickstart/SKILL.md @ 902833d826e7

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

55/100

Menjanjikan

Kepercayaan

60/100

Hanya sandbox

Audit

72/100

Berisiko

Buka audit lengkap Lihat laporan evaluasi

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
Persetujuan tinjauan AI belum ada
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: —
Hasil: —

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Detail lainnya

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      "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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: intel
Sumber: intel/skills
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan intel, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![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)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.