SKILLEMALL.ai

AB ai-career-planner

AI时代职业规划助手。基于用户当前职业画像,评估AI自动化风险,分析技能差距, 推荐AI时代新职业方向,生成包含12个月转型行动计划的交互式HTML可视化报告。 覆盖技术/产品/运营/设计/市场/行政等核心岗位类别。 Triggers: 职业规划, AI时代职业, 职业转型, 未来职业, AI替代风险, 职业方向, 转行建议, 技能提升, 职业生涯, career planning, AI career, job risk, 一人公司, 超级个体, 做什么不会被AI替代, 什么工作有前景

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 7 files body ≈ 2 417 tokens Open the sourceclawhub.ai analyzed 2 d ago

AI时代职业规划助手。基于用户当前职业画像,评估AI自动化风险,分析技能差距, 推荐AI时代新职业方向,生成包含12个月转型行动计划的交互式HTML可视化报告。 覆盖技术/产品/运营/设计/市场/行政等核心岗位类别。 Triggers: 职业规划, AI时代职业, 职业转型, 未来职业, AI替代风险, 职业方向…

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, progress reporting

ProcedureData and analyticstype 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
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "agent_created"

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 85 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2417 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 246: enough signal without eating the budget
    • +4Structure: 34 headings
    • +3Step-by-step instructions: 85 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

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

    External checks

    ClawHub: clean
    This skill is a coherent career-planning assistant that collects user-provided career details and generates a local HTML report, with no evidence of hidden persistence, credential access, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 13 Jun 2026