Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cinience/alicloud-skills --skill aliyun-platform-docs-benchmarkgit clone --depth 1 https://github.com/cinience/alicloud-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cinience/alicloud-skills/aliyun-platform-docs-benchmark)<a href="https://agentmods.dev/skills/cinience/alicloud-skills/aliyun-platform-docs-benchmark"><img src="https://agentmods.dev/badge/skills/cinience/alicloud-skills/aliyun-platform-docs-benchmark/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cinience/alicloud-skills/aliyun-platform-docs-benchmark"><img src="https://agentmods.dev/badge/skills/cinience/alicloud-skills/aliyun-platform-docs-benchmark.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.00916 |
| Opus 5 | $0.00032 | $0.00458 |
| Sonnet 5 | $0.00013 | $0.00183 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
aliyun-platform-docs-benchmark scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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>-linksL1: machine-readable official metadata/source- GCP: Discovery API
- AWS: API Models repository
- Azure: REST API Specs repository
L2: official-domain constrained web discovery fallbackL3: 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
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 127 lines · 63 tokens per session scan A 86ab5563fe39
aliyun-platform-docs-benchmark is a skill published in the GitHub repository cinience/alicloud-skills (396 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 916 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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