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-network-topology-auditgit 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-network-topology-audit)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-network-topology-audit"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-network-topology-audit/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-network-topology-audit"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-network-topology-audit.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 | $0.00149 | $0.02767 |
| Opus 5.5 | $0.00060 | $0.01107 |
| Sonnet 5.5 | $0.00030 | $0.00553 |
| Haiku 4.5 | $0.00015 | $0.00277 |
Grade A, and why
huawei-cloud-network-topology-audit 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 18d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud Network Topology Audit
Read-only orchestrator skill. This skill audits existing cloud resources — it never creates, modifies, or deletes any resource. All data collection is delegated to existing query skills (path A) or self-written scripts based on official documentation (path B).
Overview
This skill audits Huawei Cloud network topology for security and configuration risks. Given a user-described access path (e.g., "ELB web-elb → CCE prod-cce → RDS prod-db"), it:
- Resolves the real topology from actual cloud resources
- Expands to the full VPC network surface (VPCs, subnets, route tables, security groups, EIPs, ELBs, NAT gateways, ECS, RDS, CCE, DCS)
- Evaluates risks against an extensible rule directory (
rules/network-audit-rules.yaml) - Produces a Markdown audit report with a Mermaid topology diagram
Architecture
User describes access path (natural language)
│
▼
[Step 1] Connectivity precheck ── fail ──→ Prompt user to configure credentials
│ pass
▼
[Step 2] Path parsing + find-skills lookup
│
▼
[Step 3] Real topology discovery (Path A: query skills, Path B: self-written scripts)
│
▼
[Step 4] Diff: described path vs real topology (real = source of truth)
│
▼
[Step 5] User confirmation of the real topology
│
▼
[Step 6] Risk audit against network-audit-rules.yaml
│
▼
[Step 7] Markdown report generation (Mermaid diagram, ASCII fallback)
Applicable Scenarios
- User describes an access path with approximate resource names
- Current environment has configured Huawei Cloud credentials (CLI/SDK)
- Need structured audit of network topology risks
Not Applicable
- User cannot describe any resource names
- No Huawei Cloud credentials configured
- Need to modify resources (this skill is read-only)
- Cross-account audit (v1 limited to single account)
Prerequisites
- Huawei Cloud credentials — Environment variables (
HUAWEI_ACCESS_KEY/HUAWEI_SECRET_KEYorHW_AK/HW_SK/HWC_ACCESS_KEY/HWC_SECRET_KEY) or hcloud CLI config - Python 3.8+ — Required for scripts in
scripts/ - huaweicloudsdk packages — For path B resources (DCS):
pip install huaweicloudsdkdcs - Network access — To GitCode for find-skills index queries
- hcloud CLI (optional) — For CCE cluster queries via CCE management skill
What ships with it
15 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 2.3 KB
- references/cli-installation-guide.md 1008 B
- references/dataflow-diagram.md 2.2 KB
- references/iam-policies.md 1.6 KB
- references/official-doc-index.md 2.1 KB
- references/resource-skill-mapping.md 2.8 KB
- references/rule-authoring-guide.md 3.6 KB
- references/severity-definitions.md 2.1 KB
- references/verification-method.md 1.8 KB
- rules/network-audit-rules.yaml 9.5 KB
- scripts/mermaid-to-ascii.sh 1.5 KB runs code
- scripts/precheck.sh 2.4 KB runs code
- scripts/query-dcs-instances.py 3.7 KB runs code
- scripts/skill_quality_sdk.py 20 KB runs code
- scripts/validate-rules.py 3.7 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.
- 18d ago First seen · 272 lines · 149 tokens per session scan A fed8e6554b80
huawei-cloud-network-topology-audit is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (59 stars, last pushed yesterday), licensed MIT. It adds 149 tokens to every session and 2,767 once invoked, about $0.0006 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 2026-10-10.
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