菜单
Evaluate a job posting against a confirmed resume and stated job preferences, producing evidence-backed fit dimensions, hard constraints, strengths, gaps, confidence, and a recommendation. Use when ranking jobs or opening a job's match analysis.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
Propose reviewable structured edits to a local master resume from a user conversation, optionally using one job or a server-resolved local market sample as wording and prioritization context.
Classify how a trusted master resume covers a fixed list of aggregated market skills using verifiable resume paths and confirmed fact IDs.
Classify how a trusted resume covers a fixed set of job requirements using explicit resume paths and confirmed fact IDs.
Generate a focused interview preparation plan from aggregated local job-market statistics and, when available, confirmed resume facts. Use when a candidate requests interview skill priorities, personal capability gaps, preparation actions, project stories, or practice questions for the jobs stored in this app.
Extract clean, source-grounded job, company, and business-registration fields from a noisy BOSS job-detail page captured as plain text.
Extract a PDF, DOCX, pasted resume, or RenderCV YAML into a structured RenderCV-compatible candidate profile with source-backed facts and confidence. Use when importing, normalizing, or validating a resume before job matching or tailoring.
| name | job-fit |
| description | Evaluate a job posting against a confirmed resume and stated job preferences, producing evidence-backed fit dimensions, hard constraints, strengths, gaps, confidence, and a recommendation. Use when ranking jobs or opening a job's match analysis. |
Evaluate capability and desirability separately. Use only confirmed resume evidence and explicit preferences.
zh-CN).recommendation, summary, dimension label and note, hard-constraint label and note, every strengths item, and every gaps item.verdict, dimension key, and hard-constraint status must keep the English enum values from the output contract.Set a dimension score to null when evidence is insufficient. Re-normalize the overall score over known dimensions and set confidence to the sum of known weights. A failed hard constraint caps the overall score at 44.
Use verdicts: strong for 75+, good for 60–74, moderate for 45–59, weak for 30–44, and poor below 30.
{
"overallScore": 0,
"confidence": 0,
"verdict": "strong|good|moderate|weak|poor",
"recommendation": "",
"summary": "",
"dimensions": [{
"key": "technical|experience|behavior|career",
"label": "",
"score": null,
"weight": 0,
"note": "",
"evidence": [""]
}],
"hardConstraints": [{"label": "", "status": "pass|flag|fail|unknown", "note": ""}],
"strengths": [""],
"gaps": [""],
"evidence": [""],
"generatedAt": "ISO-8601",
"skillVersion": "job-fit@1.1.0"
}