Skill: cinience/aliyun-platform-docs-benchmark (version 1.0.0)

--- 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: ```bash python skills/platform/docs/aliyun-platform-docs-benchmark/scripts/benchmark_multicloud_docs_api.py --product "<product keyword>" ``` Example: ```bash 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): ```bash 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`): ```bash 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: ```bash 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: [REDACTED_SECRET] 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 ```bash 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: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Rubric: `references/review-rubric.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)