AC ljh-daren
达人选号诊断器，用六维决策表初筛达人账号，跑反作弊三查，给出可谈/压价谈/放弃的合作建议。 触发方式：/ljh-daren、「帮我看看这个达人能不能投」「这个账号数据是不是刷的」「这个号值不值得投」「达人报价合不合理」「怎么判断这个号是不是真流量」 Influencer account diagnosis tool. Screens accounts against a six-dimension scorecard and runs a three-part anti-fraud check to output a partner/negotiate/pass recommendation. Trigger: /ljh-daren, "should I book this influencer", "is this account's data fake" 评价明星艺人形象或核实合作伙伴征信之类的非达人投放场景，不要自动触发。
达人选号诊断器，用六维决策表初筛达人账号，跑反作弊三查，给出可谈/压价谈/放弃的合作建议。 触发方式：/ljh-daren、「帮我看看这个达人能不能投」「这个账号数据是不是刷的」「这个号值不值得投」「达人报价合不合理」「怎么判断这个号是不是真流量」 Influencer account diagnosis tool.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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
- note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "slug"
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. 42 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2068 tokens
- 100Running it twice. No mutating operations
- low 13 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 420: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 42 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.