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 huaweicloud/huaweicloud-skills --skill huawei-cloud-dws-spec-plannergit clone --depth 1 https://github.com/huaweicloud/huaweicloud-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/huaweicloud/huaweicloud-skills/huawei-cloud-dws-spec-planner)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-dws-spec-planner"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-dws-spec-planner/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/huaweicloud/huaweicloud-skills/huawei-cloud-dws-spec-planner"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-dws-spec-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00176 | $0.03777 |
| Opus 5.5 | $0.00070 | $0.01511 |
| Sonnet 5.5 | $0.00035 | $0.00755 |
| Haiku 4.5 | $0.00018 | $0.00378 |
Grade A, and why
huawei-cloud-dws-spec-planner 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 15d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud DWS Data Warehouse Configuration Planning and Sizing
Trigger Words
Chinese triggers: "DWS规划", "DWS选型", "DWS集群规划", "DWS节点规格", "数据仓库迁移DWS", "华为云DWS", "Data Warehouse Service", "DWS sizing", "存算一体", "存算分离", "日增数据量", "DWS扩容", "DWS容量规划"
English triggers: "DWS planning", "DWS sizing", "DWS cluster planning", "DWS node spec", "data warehouse migration to DWS", "Huawei Cloud DWS", "Data Warehouse Service", "coupled storage-compute", "decoupled storage-compute", "daily incremental data", "DWS scale-out", "DWS capacity planning"
Overview
Based on migration or greenfield scenarios, configuration planning is performed for both coupled and decoupled storage-compute architecture modes. The skill outputs one minimum-spec plan that satisfies requirements (minimum compute surplus under constraints), along with storage growth reserve and scale-out cadence recommendations based on daily incremental data. Functional boundary: requirement survey -> architecture mode confirmation -> CPU core calculation -> minimum-spec flavor selection -> hot data storage/cache capacity calculation -> (optional) growth reserve and scale-out cadence -> report output -> Excel generation.
Security rule: Specifications are sourced from Huawei Cloud official document dws_01_00018 (updated 2026-07-31). Scripts must be executed via skill action=exec; direct shell execution is prohibited.
Prerequisites
- Python3 + openpyxl (
pip install openpyxl)
Workflow
Phase 1: Requirement Survey
Collect the following key information (8 required + 2 optional):
| Category | Survey Item | Description |
|---|---|---|
| Scenario type | Migration / Greenfield | Determines CPU core calculation method |
| Data volume | Total data to warehouse (GB/TB) | Basis for CPU core and storage capacity calculation |
| Daily incremental (optional) | Daily average new data (GB/day) | Storage growth reserve and scale-out cadence; does not change node spec |
| Retention period (optional) | Retention cycle (months) | Growth prediction enabled only when daily incremental is also provided |
| Competitor info | Competitor CPU total, memory ratio | Migration scenario only; matching configuration |
| Source cluster survey (v2.4.0) | MPP cluster scale and utilization (5 groups, 20 questions) | Calibrate matching cores and hot data criteria when migrating or taking over source workload |
| Workload | Compute-intensive or not | Greenfield scenario +30% cores |
| Query characteristics | Many point queries or not | Index space +30%~50% |
| Architecture mode | Coupled / Decoupled | Determines storage calculation method and high watermark |
| Cache strategy | Cost-optimized / Performance-optimized / Hot window N days / Custom hot data volume | Decoupled only (v2.3.0) |
| Deployment region | Target region | Flavor availability |
What ships with it
12 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.
- references/acceptance-criteria.md 1.4 KB
- references/cli-installation-guide.md 5.9 KB
- references/cpu-sizing-algorithm.md 8.1 KB
- references/ecs-flavor-catalog.md 9.8 KB
- references/excel-report-template.md 16 KB
- references/iam-policies.md 896 B
- references/single-plan-algorithm.md 6.5 KB
- references/sizing-scenarios.md 9.8 KB
- references/source-cluster-survey.md 8.7 KB
- references/storage-calculation.md 7.4 KB
- references/verification-method.md 4.3 KB
- scripts/generate_dws_report.py 74 KB runs code
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.
- 15d ago First seen · 217 lines · 176 tokens per session scan A 7e33b6ec024b
huawei-cloud-dws-spec-planner is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (59 stars, last pushed yesterday), licensed MIT. It adds 176 tokens to every session and 3,777 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-10-13.
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