SKILLEMALL.ai

BD smartsheet-write

当用户请求写入数据(添加新记录或更新已有记录)时,可以使用本技能向企业微信智能表格写入数据。本技能**强制先检查并主动向用户索要** Webhook 地址和该工作表的「示例数据」schema(若缺少则立即询问并保存),之后根据 schema 精确构建 JSON payload。内置 8 个复杂度递增的完整示例、日期自动转换助手、Python requests 示例,支持所有常见字段类型。

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 103 tokens Open the sourcegithub.com analyzed 2 d ago

当用户请求写入数据(添加新记录或更新已有记录)时,可以使用本技能向企业微信智能表格写入数据。本技能强制先检查并主动向用户索要 Webhook 地址和该工作表的「示例数据」schema(若缺少则立即询问并保存),之后根据 schema 精确构建 JSON payload。内置 8…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
46/100
Unfinished process
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.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1103 tokens
  • 100Running it twice. No mutating operations
  • low 10 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 195: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (12 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.