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dpnp-troubleshooting

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Price unconfirmed★ 21 GitHub starsRegistry updated · Sep 30, 2026agent-skill

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dpnp troubleshooting

Purpose

Turns a dpnp failure into the next command to run. Covers the five things that actually go wrong: an API that is not implemented, an import that cannot find the SYCL runtime, no visible device, a device that is not the one expected, and code that got slower instead of faster.

Prefer this skill over reading the traceback and guessing. Each symptom below has a check that produces an answer, and most of them are one line.

When to Use This Skill

Use this skill when:

dpnp raises NotImplementedError, AttributeError, or a TypeError about a keyword argument.
import dpnp fails, or fails on a missing libsycl shared object.
No SYCL device is found, or dpctl lists a different device than expected.
dpnp code is slower than the NumPy it replaced.
Another library rejects a dpnp array.

Do not use this skill to plan a migration (dpnp-quickstart), to size a workload against device memory (dpnp-memory), or as a source of speedup figures.

Quick Start

Three commands answer most questions before any code changes:

python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"

Version, what is visible, and where an array actually lands. Report what they print rather than what they were expected to print.

Implementation Guide

NotImplementedError or a rejected keyword. dpnp implements a subset of NumPy, and coverage is per keyword argument as well as per function — a function that exists can still reject a signature. Do not gate on hasattr(dpnp, "name"); the attribute can be there and the call still fail. Guard the call instead:

import dpnp
import numpy

def safe_call(device_func, host_func, x):
    try:
        return device_func(x)
    except (NotImplementedError, AttributeError, TypeError):
        host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
        return dpnp.array(host_func(host))

unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))

Import failures. ImportError on the module name means it is not installed; OSError on libsycl.so means the package is there and the SYCL runtime is not. Install from the Intel conda channel, or the runtime alone from pip:
```
conda install -c https://software.repos.intel.com/python/conda \
  -c conda-forge --override-channels dpnp dpctl

pip install intel-cmplr-lib-rt      # SYCL runtime only
```
No device, or the wrong device. dpctl.get_devices() returning an empty list means the driver stack is not visible to SYCL; dpnp then has only the host to fall back to. To pin execution while debugging, select the device explicitly at allocation, which is clearer than relying on process-wide state:
```
import dpctl
import dpnp

cpu = dpctl.SyclDevice("opencl:cpu:0")
arr = dpnp.arange(1000, device=cpu)
```
The same restriction from outside the process is ONEAPI_DEVICE_SELECTOR=opencl:cpu (it replaced the older SYCL_DEVICE_FILTER, which no longer has an effect on current runtimes).
Slower than NumPy. Three causes, in the order they occur:
- The array is too small, and dispatch dominates. Below roughly a thousand elements NumPy is the right answer; dpnp earns its keep on large arrays.
- The first call was timed. It includes compilation, so time the second:
```
import time
import dpnp

x = dpnp.random.randn(100000)
dpnp.sin(x)                                  # warm up, discard
start = time.perf_counter()
dpnp.sin(x)
print(f"{time.perf_counter() - start:.4f}s")
```
- A conversion sits inside the loop. dpnp.asnumpy() copies device to host every call; hoist it above the loop, or keep the whole loop on the device.
Another library rejects the array. pandas, scikit-learn, PyTorch, and TensorFlow check for a NumPy array and refuse anything else. Convert once at the boundary with dpnp.asnumpy() — dpnp-interop has the per-library patterns.

Performance

No measured numbers ship with this skill, and a fix here is not evidence of a speedup. When a change is meant to make something faster, measure it:

Warm up first, then time the steady state.
Compare against the NumPy original on the same inputs and dtype.
Time the whole pipeline, including conversions — a loop body that got faster while the surrounding transfers got more frequent is a net loss.

Gotchas & Limitations

hasattr is not a coverage check. The attribute can exist and the call still raise. try/except is the only reliable gate.
A fallback that converts inside a loop is its own bug. Correct, and slower than never having moved to dpnp.
The default device is whatever is visible. Code that runs on a GPU workstation lands on a CPU in CI without raising, so "it worked locally" says nothing about where it ran.
intel-cmplr-lib-rt fixes the runtime, not the driver. A GPU that the kernel driver does not expose stays invisible whatever is installed in the environment.
Not covered: driver installation, container device passthrough, and multi-GPU scheduling.

References

File	Load it when
`references/official-sources.md`	you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory

Two things here should never be answered from memory: which install channel and package names are current, and whether a given NumPy API is covered in the user's release. Both are documented upstream and both have already changed.

View source ↗

File metadata

name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"

View original text

---
name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"
---

# dpnp troubleshooting

## Purpose

Turns a `dpnp` failure into the next command to run. Covers the five things that
actually go wrong: an API that is not implemented, an import that cannot find the
SYCL runtime, no visible device, a device that is not the one expected, and code
that got slower instead of faster.

Prefer this skill over reading the traceback and guessing. Each symptom below has
a check that produces an answer, and most of them are one line.

## When to Use This Skill

Use this skill when:

- `dpnp` raises `NotImplementedError`, `AttributeError`, or a `TypeError` about a
  keyword argument.
- `import dpnp` fails, or fails on a missing `libsycl` shared object.
- No SYCL device is found, or `dpctl` lists a different device than expected.
- `dpnp` code is slower than the NumPy it replaced.
- Another library rejects a `dpnp` array.

Do **not** use this skill to plan a migration (`dpnp-quickstart`), to size a
workload against device memory (`dpnp-memory`), or as a source of speedup
figures.

## Quick Start

Three commands answer most questions before any code changes:

```bash
python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"
```

Version, what is visible, and where an array actually lands. Report what they
print rather than what they were expected to print.

## Implementation Guide

1. **`NotImplementedError` or a rejected keyword.** `dpnp` implements a subset of
   NumPy, and coverage is per keyword argument as well as per function — a
   function that exists can still reject a signature. Do not gate on
   `hasattr(dpnp, "name")`; the attribute can be there and the call still fail.
   Guard the call instead:

   ```python
   import dpnp
   import numpy

   def safe_call(device_func, host_func, x):
       try:
           return device_func(x)
       except (NotImplementedError, AttributeError, TypeError):
           host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
           return dpnp.array(host_func(host))

   unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))
   ```

2. **Import failures.** `ImportError` on the module name means it is not
   installed; `OSError` on `libsycl.so` means the package is there and the SYCL
   runtime is not. Install from the Intel conda channel, or the runtime alone
   from pip:

   ```bash
   conda install -c https://software.repos.intel.com/python/conda \
     -c conda-forge --override-channels dpnp dpctl

   pip install intel-cmplr-lib-rt      # SYCL runtime only
   ```

3. **No device, or the wrong device.** `dpctl.get_devices()` returning an empty
   list means the driver stack is not visible to SYCL; `dpnp` then has only the
   host to fall back to. To pin execution while debugging, select the device
   explicitly at allocation, which is clearer than relying on process-wide state:

   ```python
   import dpctl
   import dpnp

   cpu = dpctl.SyclDevice("opencl:cpu:0")
   arr = dpnp.arange(1000, device=cpu)
   ```

   The same restriction from outside the process is
   `ONEAPI_DEVICE_SELECTOR=opencl:cpu` (it replaced the older
   `SYCL_DEVICE_FILTER`, which no longer has an effect on current runtimes).

4. **Slower than NumPy.** Three causes, in the order they occur:

   - The array is too small, and dispatch dominates. Below roughly a thousand
     elements NumPy is the right answer; `dpnp` earns its keep on large arrays.
   - The first call was timed. It includes compilation, so time the second:

     ```python
     import time
     import dpnp

     x = dpnp.random.randn(100000)
     dpnp.sin(x)                                  # warm up, discard
     start = time.perf_counter()
     dpnp.sin(x)
     print(f"{time.perf_counter() - start:.4f}s")
     ```

   - A conversion sits inside the loop. `dpnp.asnumpy()` copies device to host
     every call; hoist it above the loop, or keep the whole loop on the device.

5. **Another library rejects the array.** pandas, scikit-learn, PyTorch, and
   TensorFlow check for a NumPy array and refuse anything else. Convert once at
   the boundary with `dpnp.asnumpy()` — `dpnp-interop` has the per-library
   patterns.

## Performance

No measured numbers ship with this skill, and a fix here is not evidence of a
speedup. When a change is meant to make something faster, measure it:

- Warm up first, then time the steady state.
- Compare against the NumPy original on the same inputs and dtype.
- Time the whole pipeline, including conversions — a loop body that got faster
  while the surrounding transfers got more frequent is a net loss.

## Gotchas & Limitations

- **`hasattr` is not a coverage check.** The attribute can exist and the call
  still raise. `try`/`except` is the only reliable gate.
- **A fallback that converts inside a loop is its own bug.** Correct, and slower
  than never having moved to `dpnp`.
- **The default device is whatever is visible.** Code that runs on a GPU
  workstation lands on a CPU in CI without raising, so "it worked locally" says
  nothing about where it ran.
- **`intel-cmplr-lib-rt` fixes the runtime, not the driver.** A GPU that the
  kernel driver does not expose stays invisible whatever is installed in the
  environment.
- Not covered: driver installation, container device passthrough, and multi-GPU
  scheduling.

## References

| File | Load it when |
|---|---|
| [`references/official-sources.md`](references/official-sources.md) | you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory |

Two things here should never be answered from memory: **which install channel and
package names are current**, and **whether a given NumPy API is covered in the
user's release**. Both are documented upstream and both have already changed.

Review the source

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License: Apache-2.0
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Review before install: Avoid automatic install

License: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: intel/skills
License: Apache-2.0
Version: 1.0
Last GitHub push: Sep 29, 2026
Registry updated: Sep 30, 2026
Instruction path: skills/dpnp-troubleshooting/SKILL.md @ 902833d826e7

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

Promising

Trust

54/100

Do not auto-install

Audit

69/100

Needs review

Open full audit View eval report

Dependency or permission surface needs review
Permission surface may require sandboxing
Low GitHub adoption signal
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

Verified installs: —
Outcomes: —

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More details

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dpnp-troubleshooting

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Price unconfirmed★ 21 GitHub starsRegistry updated · Sep 30, 2026agent-skill

Overview

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Source documentation, not instructions for this website. Review permissions before running any commands.

dpnp troubleshooting

Purpose

Prefer this skill over reading the traceback and guessing. Each symptom below has a check that produces an answer, and most of them are one line.

When to Use This Skill

Use this skill when:

dpnp raises NotImplementedError, AttributeError, or a TypeError about a keyword argument.
import dpnp fails, or fails on a missing libsycl shared object.
No SYCL device is found, or dpctl lists a different device than expected.
dpnp code is slower than the NumPy it replaced.
Another library rejects a dpnp array.

Do not use this skill to plan a migration (dpnp-quickstart), to size a workload against device memory (dpnp-memory), or as a source of speedup figures.

Quick Start

Three commands answer most questions before any code changes:

python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"

Version, what is visible, and where an array actually lands. Report what they print rather than what they were expected to print.

Implementation Guide

import dpnp
import numpy

def safe_call(device_func, host_func, x):
    try:
        return device_func(x)
    except (NotImplementedError, AttributeError, TypeError):
        host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
        return dpnp.array(host_func(host))

unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))

Import failures. ImportError on the module name means it is not installed; OSError on libsycl.so means the package is there and the SYCL runtime is not. Install from the Intel conda channel, or the runtime alone from pip:
```
conda install -c https://software.repos.intel.com/python/conda \
  -c conda-forge --override-channels dpnp dpctl

pip install intel-cmplr-lib-rt      # SYCL runtime only
```
No device, or the wrong device. dpctl.get_devices() returning an empty list means the driver stack is not visible to SYCL; dpnp then has only the host to fall back to. To pin execution while debugging, select the device explicitly at allocation, which is clearer than relying on process-wide state:
```
import dpctl
import dpnp

cpu = dpctl.SyclDevice("opencl:cpu:0")
arr = dpnp.arange(1000, device=cpu)
```
The same restriction from outside the process is ONEAPI_DEVICE_SELECTOR=opencl:cpu (it replaced the older SYCL_DEVICE_FILTER, which no longer has an effect on current runtimes).
Slower than NumPy. Three causes, in the order they occur:
- The array is too small, and dispatch dominates. Below roughly a thousand elements NumPy is the right answer; dpnp earns its keep on large arrays.
- The first call was timed. It includes compilation, so time the second:
```
import time
import dpnp

x = dpnp.random.randn(100000)
dpnp.sin(x)                                  # warm up, discard
start = time.perf_counter()
dpnp.sin(x)
print(f"{time.perf_counter() - start:.4f}s")
```
- A conversion sits inside the loop. dpnp.asnumpy() copies device to host every call; hoist it above the loop, or keep the whole loop on the device.
Another library rejects the array. pandas, scikit-learn, PyTorch, and TensorFlow check for a NumPy array and refuse anything else. Convert once at the boundary with dpnp.asnumpy() — dpnp-interop has the per-library patterns.

Performance

No measured numbers ship with this skill, and a fix here is not evidence of a speedup. When a change is meant to make something faster, measure it:

Warm up first, then time the steady state.
Compare against the NumPy original on the same inputs and dtype.
Time the whole pipeline, including conversions — a loop body that got faster while the surrounding transfers got more frequent is a net loss.

Gotchas & Limitations

hasattr is not a coverage check. The attribute can exist and the call still raise. try/except is the only reliable gate.
A fallback that converts inside a loop is its own bug. Correct, and slower than never having moved to dpnp.
The default device is whatever is visible. Code that runs on a GPU workstation lands on a CPU in CI without raising, so "it worked locally" says nothing about where it ran.
intel-cmplr-lib-rt fixes the runtime, not the driver. A GPU that the kernel driver does not expose stays invisible whatever is installed in the environment.
Not covered: driver installation, container device passthrough, and multi-GPU scheduling.

References

File	Load it when
`references/official-sources.md`	you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory

View source ↗

File metadata

name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"

View original text

---
name: dpnp-troubleshooting
description: >-
  Diagnosing dpnp failures on Intel CPUs and GPUs. Use when dpnp raises
  NotImplementedError or an unexpected TypeError, when the import fails or a SYCL
  runtime library is missing, when no SYCL device is visible, when dpctl reports a
  device the user did not expect, or when dpnp code runs slower than the NumPy it
  replaced. Covers the fallback pattern for unimplemented APIs, install repair,
  forcing CPU execution, and the handoff to libraries that only accept NumPy
  arrays.
license: Apache-2.0
compatibility: "Requires dpnp and dpctl. Install commands assume conda or pip with the Intel channel or index."
metadata:
  intel-skill-type: "tool-skill"
  version: "1.0"
---

# dpnp troubleshooting

## Purpose

Turns a `dpnp` failure into the next command to run. Covers the five things that
actually go wrong: an API that is not implemented, an import that cannot find the
SYCL runtime, no visible device, a device that is not the one expected, and code
that got slower instead of faster.

Prefer this skill over reading the traceback and guessing. Each symptom below has
a check that produces an answer, and most of them are one line.

## When to Use This Skill

Use this skill when:

- `dpnp` raises `NotImplementedError`, `AttributeError`, or a `TypeError` about a
  keyword argument.
- `import dpnp` fails, or fails on a missing `libsycl` shared object.
- No SYCL device is found, or `dpctl` lists a different device than expected.
- `dpnp` code is slower than the NumPy it replaced.
- Another library rejects a `dpnp` array.

Do **not** use this skill to plan a migration (`dpnp-quickstart`), to size a
workload against device memory (`dpnp-memory`), or as a source of speedup
figures.

## Quick Start

Three commands answer most questions before any code changes:

```bash
python -c "import dpnp; print(dpnp.__version__)"
python -c "import dpctl; print([d.filter_string for d in dpctl.get_devices()])"
python -c "import dpnp; print(dpnp.arange(4).sycl_device)"
```

Version, what is visible, and where an array actually lands. Report what they
print rather than what they were expected to print.

## Implementation Guide

1. **`NotImplementedError` or a rejected keyword.** `dpnp` implements a subset of
   NumPy, and coverage is per keyword argument as well as per function — a
   function that exists can still reject a signature. Do not gate on
   `hasattr(dpnp, "name")`; the attribute can be there and the call still fail.
   Guard the call instead:

   ```python
   import dpnp
   import numpy

   def safe_call(device_func, host_func, x):
       try:
           return device_func(x)
       except (NotImplementedError, AttributeError, TypeError):
           host = dpnp.asnumpy(x) if isinstance(x, dpnp.ndarray) else x
           return dpnp.array(host_func(host))

   unique = safe_call(dpnp.unique, numpy.unique, dpnp.array([1, 2, 2, 3]))
   ```

2. **Import failures.** `ImportError` on the module name means it is not
   installed; `OSError` on `libsycl.so` means the package is there and the SYCL
   runtime is not. Install from the Intel conda channel, or the runtime alone
   from pip:

   ```bash
   conda install -c https://software.repos.intel.com/python/conda \
     -c conda-forge --override-channels dpnp dpctl

   pip install intel-cmplr-lib-rt      # SYCL runtime only
   ```

3. **No device, or the wrong device.** `dpctl.get_devices()` returning an empty
   list means the driver stack is not visible to SYCL; `dpnp` then has only the
   host to fall back to. To pin execution while debugging, select the device
   explicitly at allocation, which is clearer than relying on process-wide state:

   ```python
   import dpctl
   import dpnp

   cpu = dpctl.SyclDevice("opencl:cpu:0")
   arr = dpnp.arange(1000, device=cpu)
   ```

   The same restriction from outside the process is
   `ONEAPI_DEVICE_SELECTOR=opencl:cpu` (it replaced the older
   `SYCL_DEVICE_FILTER`, which no longer has an effect on current runtimes).

4. **Slower than NumPy.** Three causes, in the order they occur:

   - The array is too small, and dispatch dominates. Below roughly a thousand
     elements NumPy is the right answer; `dpnp` earns its keep on large arrays.
   - The first call was timed. It includes compilation, so time the second:

     ```python
     import time
     import dpnp

     x = dpnp.random.randn(100000)
     dpnp.sin(x)                                  # warm up, discard
     start = time.perf_counter()
     dpnp.sin(x)
     print(f"{time.perf_counter() - start:.4f}s")
     ```

   - A conversion sits inside the loop. `dpnp.asnumpy()` copies device to host
     every call; hoist it above the loop, or keep the whole loop on the device.

5. **Another library rejects the array.** pandas, scikit-learn, PyTorch, and
   TensorFlow check for a NumPy array and refuse anything else. Convert once at
   the boundary with `dpnp.asnumpy()` — `dpnp-interop` has the per-library
   patterns.

## Performance

No measured numbers ship with this skill, and a fix here is not evidence of a
speedup. When a change is meant to make something faster, measure it:

- Warm up first, then time the steady state.
- Compare against the NumPy original on the same inputs and dtype.
- Time the whole pipeline, including conversions — a loop body that got faster
  while the surrounding transfers got more frequent is a net loss.

## Gotchas & Limitations

- **`hasattr` is not a coverage check.** The attribute can exist and the call
  still raise. `try`/`except` is the only reliable gate.
- **A fallback that converts inside a loop is its own bug.** Correct, and slower
  than never having moved to `dpnp`.
- **The default device is whatever is visible.** Code that runs on a GPU
  workstation lands on a CPU in CI without raising, so "it worked locally" says
  nothing about where it ran.
- **`intel-cmplr-lib-rt` fixes the runtime, not the driver.** A GPU that the
  kernel driver does not expose stays invisible whatever is installed in the
  environment.
- Not covered: driver installation, container device passthrough, and multi-GPU
  scheduling.

## References

| File | Load it when |
|---|---|
| [`references/official-sources.md`](references/official-sources.md) | you need the current install channels, the API coverage of the installed release, or the device selection environment variables — all three change between releases and must not be answered from memory |

Two things here should never be answered from memory: **which install channel and
package names are current**, and **whether a given NumPy API is covered in the
user's release**. Both are documented upstream and both have already changed.

Review the source

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License: Apache-2.0

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GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
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Source & usage notes

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Source repository: intel/skills
License: Apache-2.0
Version: 1.0
Last GitHub push: Sep 29, 2026
Registry updated: Sep 30, 2026
Instruction path: skills/dpnp-troubleshooting/SKILL.md @ 902833d826e7

Version reported in registry metadata; check source releases before relying on it.

Quality

55/100

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Do not auto-install

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Open full audit View eval report

Dependency or permission surface needs review
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Low GitHub adoption signal
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Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 21 GitHub stars
Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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    "score": 62,
    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "21 GitHub stars",
      "repoActivity": "21 stars, 9 forks",
      "lastPushed": "5d since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/intel/skills/tree/main/skills/dpnp-troubleshooting",
      "install": "npx skills add intel/skills --skill dpnp-troubleshooting",
      "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"
    },
    "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": [
      "automation",
      "agent-skill"
    ],
    "known_risks": [
      "AI review approval is missing",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "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": 69,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Low GitHub adoption signal",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 21 GitHub stars",
      "Stars/forks activity: 21 stars, 9 forks; issue activity unavailable in current metadata"
    ]
  },
  "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": "Data, BI, and analytics",
    "scenario": "Browser automation",
    "maintenance": "5d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use dpnp-troubleshooting 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: 62/100 Manual review",
      "Audit: 69/100 Needs review",
      "Safety: 25/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "intel-dpnp-troubleshooting (dpnp-troubleshooting)",
      "install_command": "npx skills add intel/skills --skill dpnp-troubleshooting",
      "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": "intel-dpnp-troubleshooting",
      "task": "Use dpnp-troubleshooting 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-troubleshooting",
    "api": "https://www.openagentskill.com/api/agent/skills/intel-dpnp-troubleshooting",
    "audit": "https://www.openagentskill.com/skills/intel-dpnp-troubleshooting/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=intel-dpnp-troubleshooting&task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20dpnp-troubleshooting%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/intel-dpnp-troubleshooting/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/intel-dpnp-troubleshooting"
  }
}

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Creator: intel
Source: intel/skills
Indexed by: OpenAgentSkill community index

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Overview

dpnp troubleshooting

Purpose

When to Use This Skill

Quick Start

Implementation Guide

Performance

Gotchas & Limitations

References

Review the source

Price & running costs

Start with one small task

Source & usage notes

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Overview

dpnp troubleshooting

Purpose

When to Use This Skill

Quick Start

Implementation Guide

Performance

Gotchas & Limitations

References

Review the source

Price & running costs

Start with one small task

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal