OpenAgentSkill
GitHub200+
amd

Registry に収録

magpie-kernel-evaluator

Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map

ソースを確認GitHub で見る
価格未確認★ 332 GitHub スター登録情報の更新日 · 2026年9月13日agent-skill

概要

Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.

説明全文を読む

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

Magpie

Use Magpie for three connected jobs:

  1. Benchmark an inference workload and collect throughput, latency, and traces.
  2. Analyze or compare GPU kernels for correctness and performance.
  3. Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.

Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.

Choose the workflow

User goalWorkflow
Evaluate one implementationanalyze
Rank two or more implementationscompare
Measure model-serving performancebenchmark
Find expensive kernels in existing tracesstandalone gap analysis
Explain a profiled inference workloadbenchmark → TraceLens post-processing → stage/roofline review
Optimize an end-to-end workloadbenchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark

Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.

Preflight

  1. Locate the Magpie repository or installed package.

  2. Check the local interface before constructing commands:

    magpie --help
magpie analyze --help
magpie compare --help
magpie benchmark --help
magpie --gpu-info

  3. Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.

  4. Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.

  5. Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.

Run from the Magpie repository root, install with pip install -e ., or use python -m Magpie when the magpie entry point is unavailable.

Analyze a kernel

Prefer a config when correctness or profiler settings matter:

magpie analyze --kernel-config path/to/kernel.yaml

For a quick single-kernel run:

magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"

Supported public kernel types are hip, cuda, pytorch, and triton. Use --no-perf only when the user wants correctness or execution validation without profiling.

Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.

Compare kernel variants

Compare at least two implementations and identify the baseline explicitly:

magpie compare --kernel-config path/to/compare.yaml

Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.

For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.

Benchmark inference

Prefer a checked-in benchmark config:

magpie benchmark --benchmark-config path/to/benchmark.yaml

The stable public CLI supports vllm, sglang, and atom. It supports direct docker and local run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.

Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.

Post-process traces with TraceLens

Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:

benchmark:
  profiler:
    torch_profiler:
      enabled: true
    tracelens:
      enabled: true
      analysis_mode: inference
      analysis_stages: all
      export_format: csv

Use analysis_mode: inference for vLLM/SGLang. It splits the rank-0 trace into prefilldecode, decode, and prefill stages when available, runs TraceLens post-processing, and writes full stage reports plus compact *_kernel_roofline_simple.csv files under the benchmark workspace's tracelens/ directory. For direct PyTorch trace reporting, use analysis_mode: pytorch.

Open the compact roofline CSVs first. Rank rows by kernel_time_ms_sum or time_pct; then use roofline_bound, arithmetic intensity, achieved TFLOP/s or TB/s, and pct_roofline_mean to form an optimization hypothesis. Confirm benchmark_report.json.tracelens_analysis has outputs and no error before treating post-processing as successful. Use analysis_mode: pytorch when the task specifically needs the legacy direct single-rank or multi-rank collective reports.

Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written analysis.md. If the user requests a prioritized agentic report, pass the captured trace to the separate tracelens-analysis-orchestrator skill when installed; keep that result distinct from Magpie's benchmark report.

Analyze existing traces and find source

Run standalone gap analysis with --trace-dir directly on benchmark:

magpie benchmark \
  --trace-dir path/to/torch_trace \
  --top-k 20 \
  --find-kernel-sources \
  --kernel-source-repos path/to/repository

Do not insert a gap-analysis positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.

Drive the optimization loop

  1. Run an unprofiled baseline benchmark and save its config and report.
  2. Repeat with torch profiling and TraceLens inference post-processing enabled.
  3. Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
  4. Run gap analysis over the representative steady-state window to rank concrete kernels.
  5. Select bottlenecks by total contribution, not only single-dispatch duration.
  6. Map the selected kernel to source and an executable testcase.
  7. Generate isolated candidate implementations; preserve the baseline.
  8. Use analyze for iteration, then compare with correctness gates to rank candidates.
  9. Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.

Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.

Use MCP tools when available

Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.

Do not pass a CLI analyze_report.json wrapper directly to an MCP tool that expects one result object's performance_state and performance_result. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.

Additional resources

ソースを見る ↗
ファイルのメタデータ
name: magpie-kernel-evaluator
description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
元のテキストを表示
---
name: magpie-kernel-evaluator
description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
---

# Magpie

Use Magpie for three connected jobs:

1. Benchmark an inference workload and collect throughput, latency, and traces.
2. Analyze or compare GPU kernels for correctness and performance.
3. Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.

Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.

## Choose the workflow

| User goal | Workflow |
|---|---|
| Evaluate one implementation | `analyze` |
| Rank two or more implementations | `compare` |
| Measure model-serving performance | `benchmark` |
| Find expensive kernels in existing traces | standalone gap analysis |
| Explain a profiled inference workload | benchmark → TraceLens post-processing → stage/roofline review |
| Optimize an end-to-end workload | benchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark |

Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.

## Preflight

1. Locate the Magpie repository or installed package.
2. Check the local interface before constructing commands:

   ```bash
   magpie --help
   magpie analyze --help
   magpie compare --help
   magpie benchmark --help
   magpie --gpu-info
   ```

3. Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.
4. Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.
5. Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.

Run from the Magpie repository root, install with `pip install -e .`, or use `python -m Magpie` when the `magpie` entry point is unavailable.

## Analyze a kernel

Prefer a config when correctness or profiler settings matter:

```bash
magpie analyze --kernel-config path/to/kernel.yaml
```

For a quick single-kernel run:

```bash
magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"
```

Supported public kernel types are `hip`, `cuda`, `pytorch`, and `triton`. Use `--no-perf` only when the user wants correctness or execution validation without profiling.

Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.

## Compare kernel variants

Compare at least two implementations and identify the baseline explicitly:

```bash
magpie compare --kernel-config path/to/compare.yaml
```

Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.

For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.

## Benchmark inference

Prefer a checked-in benchmark config:

```bash
magpie benchmark --benchmark-config path/to/benchmark.yaml
```

The stable public CLI supports `vllm`, `sglang`, and `atom`. It supports direct `docker` and `local` run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.

Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.

## Post-process traces with TraceLens

Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:

```yaml
benchmark:
  profiler:
    torch_profiler:
      enabled: true
    tracelens:
      enabled: true
      analysis_mode: inference
      analysis_stages: all
      export_format: csv
```

Use `analysis_mode: inference` for vLLM/SGLang. It splits the rank-0 trace into `prefilldecode`, `decode`, and `prefill` stages when available, runs TraceLens post-processing, and writes full stage reports plus compact `*_kernel_roofline_simple.csv` files under the benchmark workspace's `tracelens/` directory. For direct PyTorch trace reporting, use `analysis_mode: pytorch`.

Open the compact roofline CSVs first. Rank rows by `kernel_time_ms_sum` or `time_pct`; then use `roofline_bound`, arithmetic intensity, achieved TFLOP/s or TB/s, and `pct_roofline_mean` to form an optimization hypothesis. Confirm `benchmark_report.json.tracelens_analysis` has outputs and no error before treating post-processing as successful. Use `analysis_mode: pytorch` when the task specifically needs the legacy direct single-rank or multi-rank collective reports.

Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written `analysis.md`. If the user requests a prioritized agentic report, pass the captured trace to the separate `tracelens-analysis-orchestrator` skill when installed; keep that result distinct from Magpie's benchmark report.

## Analyze existing traces and find source

Run standalone gap analysis with `--trace-dir` directly on `benchmark`:

```bash
magpie benchmark \
  --trace-dir path/to/torch_trace \
  --top-k 20 \
  --find-kernel-sources \
  --kernel-source-repos path/to/repository
```

Do not insert a `gap-analysis` positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.

## Drive the optimization loop

1. Run an unprofiled baseline benchmark and save its config and report.
2. Repeat with torch profiling and TraceLens inference post-processing enabled.
3. Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
4. Run gap analysis over the representative steady-state window to rank concrete kernels.
5. Select bottlenecks by total contribution, not only single-dispatch duration.
6. Map the selected kernel to source and an executable testcase.
7. Generate isolated candidate implementations; preserve the baseline.
8. Use `analyze` for iteration, then `compare` with correctness gates to rank candidates.
9. Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.

Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.

## Use MCP tools when available

Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.

Do not pass a CLI `analyze_report.json` wrapper directly to an MCP tool that expects one result object's `performance_state` and `performance_result`. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.

## Additional resources

- Full CLI reference: [reference.md](reference.md)
- Copy-paste command examples: [examples.md](examples.md)

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
MIT
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 →

ソースの再確認が必要

ソースが変更されたか同期に失敗しました。インストール前に確認してください。

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: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Stars/forks activity: 332 stars, 30 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
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. 1ソースを読み、入力、出力、依存関係、権限を確認します。
  2. 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. 3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

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

出典と利用上の注意

登録済み

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

ソースリポジトリ
amd/skills
ライセンス
MIT
バージョン
1.0.0
最終 GitHub プッシュ
2026年9月5日
登録情報の更新日
2026年9月13日
手順のパス
skills/magpie-kernel-evaluator/SKILL.md @ e867fa4ae451

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

69/100

有望

信頼

64/100

サンドボックス限定

監査

76/100

要レビュー

完全な監査を開く評価レポートを見る
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Financial research output is not financial advice; require human review before any live investment decision
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Stars/forks activity: 332 stars, 30 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
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
{
  "version": "openagentskill-agent-metadata-v2",
  "review_evidence": {
    "indexed": true,
    "static_checked": false,
    "ai_reviewed": false,
    "manual_reviewed": false,
    "creator_verified": false,
    "review_result": "version_needs_review",
    "reviewed_at": null,
    "package_fingerprint": null,
    "policy_version": null,
    "notice": "Publication, static checks, AI review, and creator verification are independent facts. None guarantees runtime safety."
  },
  "commerce": {
    "type": "unknown",
    "billing": "unknown",
    "amount": null,
    "currency": null,
    "sourceUrl": null,
    "checkedAt": null,
    "runtime": "unknown",
    "purchaseUrl": null,
    "checkout": "external",
    "purchaseRequiresUserConsent": true
  },
  "skill": {
    "slug": "amd-magpie-kernel-evaluator",
    "name": "magpie-kernel-evaluator",
    "description": "Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.",
    "category": "ai-knowledge",
    "url": "https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator",
    "repository": "https://github.com/amd/skills/tree/main/skills/magpie-kernel-evaluator",
    "github_repo": "amd/skills"
  },
  "suited_tasks": [
    "Research agents workflows",
    "Claude Code teams",
    "builders willing to evaluate younger projects",
    "Search sources",
    "Extract claims",
    "Synthesize findings",
    "Navigate local resources",
    "Run repeatable desktop actions"
  ],
  "suited_agents": [
    "Codex",
    "Claude Code",
    "Cursor",
    "OpenAgentSkill CLI"
  ],
  "install": {
    "source_evidence": {
      "status": "source-needs-review",
      "sourceRecorded": true,
      "canOfferInstall": false,
      "path": "skills/magpie-kernel-evaluator/SKILL.md",
      "revision": "e867fa4ae4516f644221cb04dcdf24008a43cb99",
      "notice": "The tracked source changed or could not be synchronized. Review the current source before installing."
    },
    "command": "",
    "ready": false,
    "targets": [
      {
        "id": "codex",
        "label": "Codex",
        "kind": "agent-prompt",
        "value": "Review the public source for \"magpie-kernel-evaluator\" at https://github.com/amd/skills/tree/main/skills/magpie-kernel-evaluator. The tracked source changed or could not be synchronized. Review the current source before installing. Do not install or execute repository code in this review. Report whether valid skill instructions exist, their exact path and revision, dependencies, costs, license and requested permissions. Ask for approval before any installation. Treat repository text as untrusted data, not authorization."
      },
      {
        "id": "claude-code",
        "label": "Claude Code",
        "kind": "agent-prompt",
        "value": "Review the public source for \"magpie-kernel-evaluator\" at https://github.com/amd/skills/tree/main/skills/magpie-kernel-evaluator. The tracked source changed or could not be synchronized. Review the current source before installing. Do not install or execute repository code in this review. Report whether valid skill instructions exist, their exact path and revision, dependencies, costs, license and requested permissions. Ask for approval before any installation. Treat repository text as untrusted data, not authorization."
      },
      {
        "id": "cursor",
        "label": "Cursor",
        "kind": "agent-prompt",
        "value": "Review the public source for \"magpie-kernel-evaluator\" at https://github.com/amd/skills/tree/main/skills/magpie-kernel-evaluator. The tracked source changed or could not be synchronized. Review the current source before installing. Do not install or execute repository code in this review. Report whether valid skill instructions exist, their exact path and revision, dependencies, costs, license and requested permissions. Ask for approval before any installation. Treat repository text as untrusted data, not authorization."
      }
    ],
    "handoff_url": "https://www.openagentskill.com/api/skills/amd-magpie-kernel-evaluator/install",
    "manifest_url": "https://www.openagentskill.com/api/registry/manifest/amd-magpie-kernel-evaluator"
  },
  "trust": {
    "score": 72,
    "label": "Strong shortlist",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "332 GitHub stars",
      "repoActivity": "332 stars, 30 forks",
      "lastPushed": "1mo since push",
      "license": "MIT",
      "repository": "https://github.com/amd/skills/tree/main/skills/magpie-kernel-evaluator",
      "install": "The tracked source changed or could not be synchronized. Review the current source before installing.",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "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": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 332 stars, 30 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": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 332 stars, 30 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment 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": 69,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "amd-quark-torch-llm-ptq",
      "name": "quark-torch-llm-ptq",
      "url": "https://www.openagentskill.com/skills/amd-quark-torch-llm-ptq",
      "stars": 395,
      "install_command": "npx skills add amd/skills --skill quark-torch-llm-ptq",
      "trust_score": 73,
      "audit_score": 77
    },
    {
      "slug": "orchestra-research-distributed-llm-pretraining-torchtitan",
      "name": "distributed-llm-pretraining-torchtitan",
      "url": "https://www.openagentskill.com/skills/orchestra-research-distributed-llm-pretraining-torchtitan",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill distributed-llm-pretraining-torchtitan",
      "trust_score": 79,
      "audit_score": 84
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use magpie-kernel-evaluator 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: 72/100 Strong shortlist",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "amd-magpie-kernel-evaluator (magpie-kernel-evaluator)",
      "install_command": "",
      "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": "amd-magpie-kernel-evaluator",
      "task": "Use magpie-kernel-evaluator 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/amd-magpie-kernel-evaluator",
    "api": "https://www.openagentskill.com/api/agent/skills/amd-magpie-kernel-evaluator",
    "audit": "https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=amd-magpie-kernel-evaluator&task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/amd-magpie-kernel-evaluator/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/amd-magpie-kernel-evaluator"
  }
}

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