SKILLEMALL.ai

BF cloud-storage-manager-pro

面向团队与企业的全功能多云存储管控平台,在免费版基础上扩展成批跨云迁移、双向实时同步、加密密钥管控、多用户协作、智能分层存储与成本剖析报告等高级能力。核心能力:。面向团队与企业的全功能多云存储管理平台。在免费版基础上扩展批量跨云迁移、双向实时同步、加密密钥管理、多用户协作、智能分层存储与成本分析报告等8项高级能力. 功能涵盖: cloud, storage, manager。

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 2 689 tokens Open the sourceclawhub.ai analyzed 3 d ago

面向团队与企业的全功能多云存储管控平台,在免费版基础上扩展成批跨云迁移、双向实时同步、加密密钥管控、多用户协作、智能分层存储与成本剖析报告等高级能力。核心能力:。面向团队与企业的全功能多云存储管理平台。在免费版基础上扩展批量跨云迁移、双向实时同步、加密密钥管理、多用户协作、智能分层存储与成本分析报告等8项高级能力.

As a process F 35/100 · Will not run — References files that are not bundled: references/detail.md

IntegrationAWSAzureOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
56
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/detail.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
  3. The text references files that are not there: add them or drop the references.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 189 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • warning missing-ref reference to a missing file: references/detail.md
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "pricing_tier"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/detail.md
  • 0Tools and files. 1 referenced file(s) missing: references/detail.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Steps. 80 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2689 tokens
  • 100Running it twice. No mutating operations
  • low 22 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -232 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 189: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 80 items
  • +4Has examples (12 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.

External checks

ClawHub: suspicious
This cloud storage skill is mostly coherent, but it asks for powerful cloud and command-line authority while its activation scope and safety controls are too broad for automatic use.
LLM: suspicious (high) · 20 Aug 2026