SKILLEMALL.ai

BC mongodb-atlas-admin-free

|- 通过API浏览和调用文档数据库云管控平台。兼容API目录浏览、端点详情获取、 Schema定义查询和实时API调用。覆盖50+分类的完整API端点,兼容 dry-run 预检和自发确认模式. 不适用于直接数据库查询操作.该技能适用于相关开发场景,包含结构化的工作流程和配置指引.经过深度差异化处置,针对用户反馈和使用痛点进行了改进,提升了实用性和可操作性.

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 995 tokens Open the sourceclawhub.ai analyzed 3 d ago

|- 通过API浏览和调用文档数据库云管控平台。兼容API目录浏览、端点详情获取、 Schema定义查询和实时API调用。覆盖50+分类的完整API端点,兼容 dry-run 预检和自发确认模式.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationMongoDBAWSInfrastructureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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.
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 181 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
  • 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 "summary_zh"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "pricing_tier"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 995 tokens
  • 100Running it twice. No mutating operations
  • 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

  • +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
  • +2Single-language instructions
  • +3Description length 181: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (7 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: suspicious
The skill is clearly aimed at MongoDB Atlas administration, but it asks an agent to run broad live admin API actions with credentials while giving incomplete safety boundaries and packaging no referenced scripts.
LLM: suspicious (medium) · 27 Jul 2026