SKILLEMALL.ai

AD ai-talent-grader

基于简历、面试记录和JD,对候选人进行AI时代能力定级(L1-L4)。 核心能力:简历漏洞穿透审计、六维度评分卡、双乘数加权、测谎面试题生成、评分一致性校准、 面试认知复盘(v3.3 Pro 新增)。v3.3 从"简历打分器"升级为"认知行为分析系统", 评估候选人如何思考、如何应对不确定性、如何与AI协同。 Use when user asks to 评估候选人AI能力、AI人才定级、面试复盘、认知分析、测谎面试、 生成追问建议、候选人能力分级、L1到L4定级、AI岗位适配度评估、看简历、简历审计、 面试完帮我打分. 不适用于绩效评估、晋升评审、员工培训需求分析或非AI相关的能力评估.

ClawHub Agent Skills author: tuobadaidai v3.3.2 MIT-0 21 files · 1 script body ≈ 1 931 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
43/100
Unfinished process
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

    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: 21. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "label"

    Process rating: all ten parameters 43/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 101 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1931 tokens
    • low 15 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 297: enough signal without eating the budget
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 101 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This skill appears purpose-built for AI hiring assessment, but it handles sensitive candidate materials with broad auto-activation and weak data-handling disclosure.
    LLM: suspicious (high) · 24 Jul 2026