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Qualcomm Agent Skill

Build, test, or develop the QNN (Qualcomm AI Engine Direct) backend. Use when working on backends/qualcomm/, building QNN (use backends/qualcomm/scripts/build.sh), adding new ops or passes, running QNN delegate tests, or exporting models for Qualcomm HTP/GPU targets. Also exposes a Buck-vs-CMake parity workflow — invoke as `/qualcomm buck-fix`, `/qualcomm buck-cmake fix`, `/qualcomm buck-parity`, or any user request to fix `test-qnn-buck-build-linux` CI failures or check buck/cmake drift in backends/qualcomm/. Also covers QNN intermediate-output / per-layer accuracy debugging — trigger on phrases like "QNN accuracy issue", "QNN output doesn't match CPU", "debug per-layer for QNN", "find which QNN layer is wrong".

14k tokens
context cost
the whole folder, loaded on every use
6
files
instructions only
0
copies elsewhere
how many repositories repackaged it
4857
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/pytorch/executorch --skill qualcomm

What comes with it

50 201 bytes besides the instruction
buck_parity.md
lowering_export.md
model_enablement.md
new_op_development.md
qnn_intermediate_debugger.md

The instruction itself

5 sections, as written by the author

QNN (Qualcomm AI Engine Direct) Backend

Slash command argument routing

When this skill is invoked with arguments (e.g. /qualcomm <args>), classify the args FIRST and route before doing anything else:

| If args contain any of… | Route to |

|---|---|

| buck-fix, buck-cmake, buck cmake, buck-parity, buck parity, buck ci, qnn buck, fix qnn ci, test-qnn-buck-build-linux, or any natural-language request to fix QNN buck CI / catch buck-cmake drift | Read buck_parity.md and follow it end-to-end. Default mode: full iterative-fix loop. If the args also contain check or diagnose, run buck once and report only — do not apply fixes. |

| (no args) or any other args | Stay in this file; treat as a normal /qualcomm discovery request and use the Advanced Topics table below. |

Advanced Topics

When the user's request falls into one of these areas, read the corresponding file before proceeding:

| Topic | File | When to read |

|---|---|---|

| Export / lowering / quantization options / pass pipelines | lowering_export.md | User asks about exporting, lowering, quantization config, QuantDtype, QuantRecipe, pass pipelines |

| New op development | new_op_development.md | User asks to add/implement a new op or op builder |

| Model enablement | model_enablement.md | User asks to enable a new model end-to-end |

| Buck vs CMake parity (pre-PR or fix red CI) | buck_parity.md | User changed BUCK / TARGETS / targets.bzl or CMakeLists.txt under backends/qualcomm/, added new .cpp / .h / #include there, is preparing to push a PR that touches QNN, or the test-qnn-buck-build-linux CI check on their PR is red and they want to fix it locally. Direct trigger: /qualcomm buck-fix. |

| Profiling & debugging | profiling.md | User asks about profiling, optrace, QHAS, QAIRT Visualizer *(file TBD)* |

| QNN intermediate-output / per-layer accuracy debugging | qnn_intermediate_debugger.md | User reports QNN-vs-CPU accuracy divergence, asks to debug per-layer / intermediate output for QNN, mentions QNNIntermediateDebugger / QcomNumericalComparator, or wants to find which layer causes a QNN accuracy drop. Workflow generates a new debug script from the user's existing example script. |

Building

Use backends/qualcomm/scripts/build.sh. Linux only (macOS not supported).

Environment variables:

  • QNN_SDK_ROOT — path to QNN SDK (auto-downloaded if not set)
  • ANDROID_NDK_ROOT — path to Android NDK (auto-downloaded if not set)

Build targets:

| Target | Default | Build dir |

|---|---|---|

| x86_64 (Python interface + host tools) | enabled | build-x86/ |

| Android arm64-v8a (device runner) | enabled | build-android/ |

| Direct mode (LPAI ADSP or Hexagon CDSP) | disabled | build-direct/ |

| OE Linux embedded | disabled | build-oe-linux/ |

Common build commands:

# Full build (x86_64 + Android)
./backends/qualcomm/scripts/build.sh

# x86_64 only (faster, for Python interface development)
./backends/qualcomm/scripts/build.sh --skip_linux_android

# Android only (skip x86_64)
./backends/qualcomm/scripts/build.sh --skip_x86_64

# Incremental build (skip clean)
./backends/qualcomm/scripts/build.sh --no_clean

# Enable Hexagon DSP direct mode (requires HEXAGON_SDK_ROOT, HEXAGON_TOOLS_ROOT, DSP_VERSION)
./backends/qualcomm/scripts/build.sh --enable_hexagon

# OE Linux embedded target (requires TOOLCHAIN_ROOT_HOST, TOOLCHAIN_ROOT_TARGET)
./backends/qualcomm/scripts/build.sh --enable_linux_embedded

# Release build
./backends/qualcomm/scripts/build.sh --release

# Control parallelism
./backends/qualcomm/scripts/build.sh --job_number 8

After x86_64 build, the Python interface .so files are copied to backends/qualcomm/python/ automatically.

Testing

QNN_SDK_ROOT=/path/to/qnn_sdk \
ANDROID_NDK_ROOT=/path/to/android_ndk \
LD_LIBRARY_PATH=/path/to/executorch/build-x86/lib:/path/to/qnn_sdk/lib/x86_64-linux-clang \
PYTHONPATH=$(dirname $EXECUTORCH_ROOT) \
python backends/qualcomm/tests/test_qnn_delegate.py \
    TestQNNFloatingPointOperator.test_qnn_backend_abs \
    -H $HOST -s $DEVICE_SERIAL -m SM8850 -b build-android -a /path/to/artifacts

> Note (build from source): Set PYTHONPATH to the parent directory of the executorch repo root. Required because executorch.examples.qualcomm lives in the source tree and is not installed into site-packages.

Required flags: -m (SoC model), -b (Android build dir). Optional: -s (device serial), -H (host), -a (artifact dir), -c (compile only), -x (run on x86_64).

Test classes:

| Class | Description |

|---|---|

| TestQNNFloatingPointOperator | FP16 operator tests |

| TestQNNQuantizedOperator | Quantized operator tests |

| TestQNNFloatingPointModel | FP16 model-level tests |

| TestQNNQuantizedModel | Quantized model-level tests |

| TestQNNFloatingPointUtils | FP16 utility tests |

| TestQNNQuantizedUtils | Quantized utility tests |

| TestExampleLLMScript | LLM script tests |

| TestExampleMultimodalityScript | Multimodality script tests |

| TestExampleOssScript | OSS model script tests |

| TestExampleScript | General example script tests |

| TestUtilsScript | Utility script tests |

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How to use it

Copy the folder

Take pytorch/qualcomm from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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