SKILLEMALL.ai

BB huawei-cloud-waf-aad-rule-management

Manage and diagnose Huawei Cloud WAF (Web Application Firewall / Web应用防火墙) and AAD (Anti-DDoS / 抗DDoS) protection via the hcloud CLI. Covers WAF instance and protected-domain queries, policy and rule management (custom / precise protection, IP blacklist & whitelist, CC rate limiting, geo-blocking), CNAME onboarding diagnostics, rule-order and false-positive analysis, plus AAD instance listing and EIP protection-coverage checks. Read-only queries and diagnostics run automatically (R3); rule changes require preview + user confirmation (R2); rule deletion requires preview + explicit confirmation (R1). NOTE: AAD instance purchase/unsubscribe is a console-only (包周期) flow — this skill declares that limitation and offers console guidance. Use this skill when the user needs to inspect or change WAF protection, verify CNAME/DNS access to a WAF endpoint, review WAF rule order, check whether EIPs are covered by Anti-DDoS, or manage WAF rules. Triggers include: WAF, Web应用防火墙, Web Application Firewall, AAD, Anti-DDoS, 抗DDoS, DDoS防护, 防火墙, web protection, IP黑名单, IP白名单, blacklist, whitelist, CC防护, CC攻击, 限速, rate limiting, 地域封禁, geo rule, CNAME接入, WAF诊断, 规则顺序, 误报, EIP防护, 防护覆盖, waf aad, huawei-cloud-waf-aad-rule-management.

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 9 files body ≈ 5 332 tokens Open the sourceclawhub.ai analyzed 3 d ago

Manage and diagnose Huawei Cloud WAF (Web Application Firewall / Web应用防火墙) and AAD (Anti-DDoS / 抗DDoS) protection via the hcloud CLI.

As a process B 68/100 · Nearly there — weak spots: result and completion, running it twice

AnalyzerSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
B
78/100
safety, quality, tests
Safety 60%
95
Quality 40%
53
Run on models
none yet
Process rating
B
68/100
Nearly there
Running it twice w 4
30
Result and completion w 14
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Shorten the description to 1024 characters.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • medium Dangerous commands cmd-pipe-to-shell references/cli-installation-guide.md:12
    Downloads and executes remote code from an unrecognised host (pipe to shell) (documentation of a security skill)
    curl -sSL https://cn-n…com/install.sh | bash
    security skill

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1225 chars, limit 1024
  • warning body-long SKILL.md body ≈ 5332 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 68/100

  • 30Running it twice. 36 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5332 tokens
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (12 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 1225: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (6 of 6)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: suspicious
This skill’s WAF/AAD management workflow is mostly coherent, but its bundled quality-reporting SDK can transmit sensitive execution data and Huawei Cloud credential-derived tokens in ways users may not expect.
LLM: suspicious (high) · 11 Sept 2026