AD Mapping-Skill
AI/ML 人才搜索、论文作者发现、实验室成员爬取、GitHub 研究者挖掘与个性化招聘邮件生成 skill。只要用户提到查找 AI/ML PhD、研究员、工程师，抓取实验室成员、OpenReview/CVF 会议作者、GitHub 网络研究者，提取主页/Scholar/GitHub/邮箱/研究方向，识别华人、分类去重，或把结果导入飞书多维表格并批量生成邮件，就应该优先使用这个 skill；即使用户没有明确说“使用 Mapping-Skill”，只要任务属于这些复合工作流，也应触发。
As a process D 46/100 · Unfinished process — 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
- 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: 27. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription 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 (python) that frontmatter does not declare
- 100Steps. 111 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2013 tokens
- 100Running it twice. No mutating operations
- low 12 top-level sections: this looks like several domains in one skill
- medium 3 test cases, all positive: not one "should refuse" or "should ask first"
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 244: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 111 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (15 of 16)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.
External checks
ClawHub: suspicious
This skill is a disclosed recruiting scraper, but it enables broad personal-data harvesting, ethnicity inference, and bulk outreach workflows without enough user controls or privacy guardrails.
LLM: suspicious (high) · VirusTotal: · 29 May 2026