application-packager
Create a company-specific job application package from a JD, resume.md, cover letter drafts, HR review, and optional company values input. Use when the user…
Build or improve a job seeker's resume.md source file from conversational career input. Use when the user wants to create resume.md, organize new-grad or experienced career history, convert raw experiences into STAR/CAR evidence, add missing metrics, or prepare reusable career
$ npx -y skills add kyoungbinkim/give-me-job --skill resume-intake --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/resume-intakeContext preview
The summary Claude sees to decide when to auto-load this skill.
Build or improve a job seeker's resume.md source file from conversational career input. Use when the user wants to create resume.md, organize new-grad or experienced career history, convert raw experiences into STAR/CAR evidence, add missing metrics, or prepare reusable career
name: resume-intake description: Build or improve a job seeker's resume.md source file from conversational career input. Use when the user wants to create resume.md, organize new-grad or experienced career history, convert raw experiences into STAR/CAR evidence, add missing metrics, or prepare reusable career evidence for cover letters and job applications. 한국어 요청도 포함합니다 - 이력서 작성, 이력서 정리, 경력기술서, 경력 정리, 커리어 정리, 자소서 소재 정리, 내 경험 정리해줘, 신입 이력서, 경력직 이력서.
Use this skill to turn raw career material into a structured `resume.md` that other give-me-job skills can trust.
Use this skill when `resume.md` is missing, thin, outdated, or lacks evidence that can support a Korean cover letter.
Do not use this skill when the user already has a usable `resume.md` and only needs JD analysis, cover letter drafting, HR review, or packaging.
**DoF: LOW**
Follow user-provided facts only. Do not infer missing employers, dates, metrics, awards, tools, responsibilities, or outcomes.
Permitted inferences:
Prohibited inferences:
Required context:
Optional context:
Required parameters:
Outputs produced:
1. Read `references/resume-schema.md` before creating or rewriting `resume.md`. 2. Determine whether the user is `new-grad`, `experienced`, or still `unknown`. 3. Read an existing `resume.md` if present. Preserve valid facts, but migrate old `Profile`, `Core Summary`, `Experience Bank`, or `Work History` structures into the canonical sections. 4. Create or update the metadata block:
5. Collect raw experiences in the user's language first. Do not force every field before making progress, but ask for missing facts that materially affect evidence quality. 6. Build the canonical top-level sections in this order:
when they have relevant work/internship/part-time experience
7. Split work and project material into STAR/CAR bullets:
When the user states why they chose an approach, rejected an alternative, or changed direction, capture that reasoning too. Cover-letter and interview questions frequently ask how a candidate decided, and that judgment cannot be recovered later from a result-only bullet. Ask for it when an entry is the candidate's strongest evidence. Never reconstruct a rationale the user did not give. 8. End every work role and project entry with exactly one strength comment: `<!-- strength: High -->`, `<!-- strength: Medium -->`, or `<!-- strength: Low -->`. 9. Write `Summary` last. If any core work/project evidence remains `Low`, do not polish a final summary; leave `Pending stronger evidence.` and ask follow-up questions. 10. Keep claims factual. Do not invent company names, numbers, responsibilities, awards, links, tools, or outcomes.
Emphasize project depth, learning speed, role clarity, problem solving, collaboration, and job relevance. `Projects` is required even when work experience is absent. If metrics are weak, strengthen the explanation of process, decision making, and learning without pretending there was business impact.
New-grad results are usually small, so the reasoning behind them carries most of the signal. Prefer capturing how the candidate framed a problem, what they tried first, and what they changed after it did not work, over inflating the outcome. Also capture mistakes the candidate owns and what they changed afterward: 실패 경험 and 성장과정 questions are common, and an honest, resolved mistake is usable evidence that a polished result-only bullet cannot supply.
Separate responsibility from achievement. `Work Experience` is required for experienced candidates. Emphasize role scope, measurable result, business impact, cross-functional work, decision making, and repeatable contribution.
Adopt the canonical resume-style structure in `references/resume-schema.md`. Do not create a separate `Experience Bank` for new intake. Assign stable IDs such as `EXP-001` to evidence in `Work Experience` or `Projects`, preserving existing IDs. Record the source section, entry title, and current line/bullet location alongside each ID. Never renumber IDs when entries move.
For each experience capture situation, the candidate's role, action, result, learning, and suitable question types. Keep company/question reuse and related follow-up questions as usage notes, separate from career facts. Rewrite reused evidence for the selected company, role, and question purpose.
Never copy a newly generated number or claim from a cover letter into the resume. When the user provides a new fact, show the proposed factual change before updating the resume and related materials. Changed source facts require revalidation of packages that used them.
Treat mi
give-me-job is an AI agent toolkit for Korean job applications. It helps you turn scattered career notes, a Korean job post, and company context into a focused application package: JD analysis, evidence-grounded 자기소개서 drafts, HR risk review, interview
Repo: kyoungbinkim/give-me-job
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