SKILLEMALL.ai

AB huawei-cloud-swr-image-management

Huawei Cloud SWR (Software Repository for Container) image lifecycle management skill using hcloud CLI. Use this skill when the user wants to: (1) manage SWR namespaces (organizations) - create/query/delete, (2) manage image repositories - create/query/update/delete, (3) manage image tags/versions - query/create/delete, (4) obtain docker login credentials for SWR, (5) check SWR quotas and usage limits. Trigger: user mentions "SWR image management", "SWR 镜像管理", "container image", "镜像仓库", "SWR 组织", "SWR namespace", "镜像版本", "docker login", "SWR 配额", "SWR tag", "容器镜像", "镜像生命周期", "SWR repository", "SWR 登录", "SWR quota"

ClawHub Agent Skills author: shijingcheng v0.1.0 MIT-0 11 files body ≈ 5 124 tokens Open the sourceclawhub.ai analyzed 3 d ago

Huawei Cloud SWR (Software Repository for Container) image lifecycle management skill using hcloud CLI.

As a process B 70/100 · Nearly there — weak spots: failures and branches, progress reporting

IntegrationDockerSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
70/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: ClawHub, ClawHub

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5124 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 70/100

  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5124 tokens
  • 100Steps. 61 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • low 11 top-level sections: this looks like several domains in one skill

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 12 example trigger phrases
  • +3Description length 621: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 61 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (9 of 9)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

External checks

ClawHub: clean
This is a coherent Huawei Cloud SWR management guide; it handles sensitive cloud operations, but they are disclosed and mostly guarded with cautions and credential-handling notes.
LLM: benign (high) · VirusTotal: · 17 Jun 2026