BC ads-case-analyzer
内容消费行业广告投放 Case 排查助手。输入 campaign_id/advertiser_id/brand_account_id、数据分析周期和排查方向，自动拉取投后数据、出价数据，完成漏斗分析、出价链路拆解、根因推断，产出结构化 Redoc 分析文档。当用户说「帮我排查这个客户的投放」「分析一下这个计划为什么跑不起来」「这个账户消耗下跌是什么原因」时触发。
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription 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. 76 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3789 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
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 182: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 76 items
- +4Has examples (13 code blocks)
- +4Reference files are cited in the instructions (2 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.
External checks
ClawHub: clean
This skill is a coherent read-only advertising case analysis assistant, but it works with sensitive internal ad performance and bidding data that users should only access when authorized.
LLM: benign (medium) · VirusTotal: · 29 May 2026