SKILLEMALL.ai

BC check-bookings-phone

检查携程旅行app预售订单的日期可用性。通过ADB连接Android设备,自动化操作携程app,遍历"未使用 预售订单"中的每个订单,查询指定日期范围内每天的预约状态(可约、约满、补差价等)。当用户提到检查携程订单、查看预售订单可用日期、查酒店预约状态时触发此skill。

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 443 tokens Open the sourcegithub.com analyzed 2 d ago

检查携程旅行app预售订单的日期可用性。通过ADB连接Android设备,自动化操作携程app,遍历"未使用 预售订单"中的每个订单,查询指定日期范围内每天的预约状态(可约、约满、补差价等)。当用户提到检查携程订单、查看预售订单可用日期、查酒店预约状态时触发此skill。

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

Proceduretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
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.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token package-lock.json:55
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…gM8+u9qM…d10/AOvI…sU8/RA5FzDVQ==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:182
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…vsj+BJIp…OPm/R7pi…XC9/moGs…VrA==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:214
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…KYR+bWBk…ZPi/Id5f…8lQ==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:483
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…Cf0+pJg3eU9/oBuq…ZLc/1iLJ…Zbw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:649
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…7pY+zoMV…h0x/Ptw8…8dg==",
    detector

Files scanned: 4. 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 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. No external tools needed
  • 100Steps. 10 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 443 tokens
  • 100Running it twice. No mutating operations

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 136: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (6 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.