aliyun-platform-docs-benchmark

name	aliyun-platform-docs-benchmark
description	Use when benchmarking similar product documentation and API documentation across Alibaba Cloud, AWS, Azure, GCP, Tencent Cloud, Volcano Engine, and Huawei Cloud. Given one product keyword, auto-discover latest official docs/API links, score quality consistently, and output detailed prioritized improvement recommendations.
version	1.0.0

Multi-Cloud Product Docs/API Benchmark

Use this skill when the user wants cross-cloud documentation/API comparison for similar products.

Supported clouds

• Alibaba Cloud
• AWS
• Azure
• GCP
• Tencent Cloud
• Volcano Engine
• Huawei Cloud

Data source policy

• L0 (highest): user-pinned official links via --<provider>-links
• L1: machine-readable official metadata/source
  • GCP: Discovery API
  • AWS: API Models repository
  • Azure: REST API Specs repository
• L2: official-domain constrained web discovery fallback
• L3: insufficient discovery (low confidence)

Workflow

Run the benchmark script:

python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py --product "<product keyword>"

Example:

python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py --product "serverless"

LLM platform benchmark example (Bailian/Bedrock/Azure OpenAI/Vertex AI/Hunyuan/Ark/Pangu):

python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py --product "Bailian" --preset "llm-platform"

If --preset is omitted, script attempts to auto-match preset based on keyword.

Scoring weights can be switched by profile (see references/scoring.json):

python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py --product "Bailian" --preset "llm-platform" --scoring-profile "llm-platform"

Optional: pin authoritative links

Auto-discovery may miss pages. For stricter comparison, pass official links manually:

python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py \
  --product "object storage" \
  --aws-links "https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html" \
  --azure-links "https://learn.microsoft.com/azure/storage/blobs/"

Available manual flags:

• --alicloud-links
• --aws-links
• --azure-links
• --gcp-links
• --tencent-links
• --volcengine-links
• --huawei-links

Each flag accepts comma-separated URLs.

Output policy

All artifacts must be written under:

output/aliyun-platform-docs-benchmark/

Per run:

• benchmark_evidence.json
• benchmark_report.md

Reporting guidance

When answering the user:

1. Show score ranking across all providers.
2. Highlight top gaps (P0/P1/P2) and concrete fix actions.
3. If discovery confidence is low, ask user to provide pinned links and rerun.

Validation

mkdir -p output/aliyun-platform-docs-benchmark
for f in skills/platform/docs/aliyun-platform-docs-benchmark/scripts/*.py; do
  python3 -m py_compile "$f"
done
echo "py_compile_ok" > output/aliyun-platform-docs-benchmark/validate.txt

Pass criteria: command exits 0 and output/aliyun-platform-docs-benchmark/validate.txt is generated.

Output And Evidence

• Save artifacts, command outputs, and API response summaries under output/aliyun-platform-docs-benchmark/.
• Include key parameters (region/resource id/time range) in evidence files for reproducibility.

Prerequisites

• Configure least-privilege Alibaba Cloud credentials before execution.
• Prefer environment variables: ALIBABACLOUD_ACCESS_KEY_ID, ALIBABACLOUD_ACCESS_KEY_SECRET, optional ALIBABACLOUD_REGION_ID.
• If region is unclear, ask the user before running mutating operations.

References

• Rubric: references/review-rubric.md