BC alibabacloud-cloud-firewall-acl-manager
Alibaba Cloud Cloud Firewall ACL policy management tool centered on backup/restore/management. Backs up and restores ACL policies (Internet boundary, NAT boundary, VPC boundary) together with their supporting configurations (address books - custom IP, cloud asset IP, custom domain, port; sync nodes - ACK cluster, private DNS), with differential restore (only fills items missing online). Also provides ACL policy export and statistical analysis (hit analysis, duplicate rule detection, shadow rule detection, compliance audit) and single-point changes (add policy, enable/disable). 以备份/恢复/管理为核心的阿里云云防火墙ACL策略管理工具。 支持备份和恢复ACL策略（互联网边界、NAT边界、VPC边界）及其配套配置 （地址簿：自定义IP、云资产IP、自定义域名、端口，同步节点：ACK集群、私有DNS）， 支持差异恢复（只补线上缺失项）；还提供ACL策略的导出与统计分析（命中分析、 重复规则检测、影子规则检测、合规巡检）与单点变更（新增策略、启用/禁用）。 当用户提到云防火墙备份、云防火墙恢复、地址簿备份、ACL策略备份、防火墙配置 导出导入、ACL策略导出、策略命中分析、重复/影子策略检测、新增ACL策略、启用 禁用策略时使用此技能。不提供策略删除/清理，需用户在控制台手动操作；不做规则 不生效的故障诊断。
Alibaba Cloud Cloud Firewall ACL policy management tool centered on backup/restore/management.
As a process C 62/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 0
✓ No critical or high findings
Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5418 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5418 tokens
- 100Steps. 27 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 903: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 26 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.