AC talent-radar
智能人岗匹配诊断系统，适用于中文招聘场景。当用户明确要求分析特定简历与具体岗位 的匹配度、生成人才筛选报告或诊断求职差距时使用。 语言范围：主要支持中文简历和岗位描述；英文或其他语言输入可能导致分析结果不准确， 建议先翻译为中文后使用。 适用场景：企业端（招聘筛选、人才评估）和个人端（求职诊断、职业规划）。 触发条件（需同时满足）：用户明确提供简历文本或文件 AND 用户明确指定目标岗位或JD。 精确触发词：帮我分析这份简历、这份简历和岗位匹配吗、帮我生成招聘匹配报告、 诊断我的求职差距、帮我筛选候选人简历、这份简历符合岗位要求吗。 输出：匹配度分析报告、差距诊断报告、优化建议、推荐岗位/候选人列表。
智能人岗匹配诊断系统，适用于中文招聘场景。当用户明确要求分析特定简历与具体岗位 的匹配度、生成人才筛选报告或诊断求职差距时使用。 语言范围：主要支持中文简历和岗位描述；英文或其他语言输入可能导致分析结果不准确， 建议先翻译为中文后使用。 适用场景：企业端（招聘筛选、人才评估）和个人端（求职诊断、职业规划）。…
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.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Risky intent
intent-offensive-securityreferences/clawhub_audit_checklist.md:14Offensive-security / dual-use content (legitimate for authorised testing; review intended use)| Privilege Escalation | 过度权限、凭证访问 |
Files scanned: 17. 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. 93 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1326 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 304: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 93 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.