tailor-resume

Assemble the best possible resume for a specific role, drawn from the user's ExperienceLibrary. Invoke the resume-coach agent to do the work.

Workflow

1. Collect inputs

Job description — required. If the user has not provided one:

"Paste the job description and I'll tailor your resume for it."

Target role and company — extract from the JD if not stated explicitly.

ExperienceLibrary — read {user_dir}/CareerNavigator/ExperienceLibrary.json. If the file is missing or the units array is empty:

"Your ExperienceLibrary is empty. Run /career-navigator:add-source to add a resume first, then I can tailor one for this role."

Profile — read {user_dir}/CareerNavigator/profile.md for differentiators, skills, and target preferences. Do not ask the user for anything that is already in the profile.

2. Invoke resume-coach

Hand off to the resume-coach agent with:

• The full job description text
• The full ExperienceLibrary (CareerNavigator/ExperienceLibrary.json)
• The user profile (CareerNavigator/profile.md)
• Any specific instructions the user provided (emphasis, exclusions, tone)

resume-coach will:

• Select the most relevant experience units using performance_weights
• Assemble and rewrite them for the target role
• Ensure all required JD keywords are present (ATS pass)
• Return the completed resume text and an ATS score

Voice-aligned Summary (optional): If the user explicitly asks to match their LinkedIn voice / voice-profile.md for the Summary only, ask resume-coach for a ResumeSummaryBrief per agents/resume-coach/AGENT.md. Before invoking writer, run the same voice preflight as draft-outreach (ask for ## User writing samples / ## User writing samples (launch) or skip). Then invoke writer in resume-summary mode, substitute the returned ## Summary into the draft, then continue to ATS presentation below.

3. Score and review

Present the full assessment returned by resume-coach before saving:

Resume assembled for {Role} at {Company}

ATS score: {n}/100
Keyword coverage: {matched_keywords} / {total_required} must-have keywords matched

Strengths
- {What the ExperienceLibrary covers well for this role}

Gaps (honest)
- {Requirements with no or weak ExperienceLibrary coverage}
- {Achievements that need metrics or sharpening}

If the score is below 70, ask before saving:

"This resume scores {n}/100 for ATS. The main gaps are {keywords}. Do you want me to address them before saving?"

4. Save the artifact

Once the user confirms (or if the score is ≥ 70 with no gaps flagged), save the resume:

Filenames (avoid host / MCP validation errors): use ASCII-safe names only—hyphens -, no em dashes or smart quotes. Sanitize company and role: remove \ / : * ? " < > | and newlines; collapse whitespace; limit basename (~100 chars). Example: Anthropic-PM-Claude-Code-Resume-2026-03-24.md

• Filename format: {SanitizedCompany}-{SanitizedRole}-Resume-{YYYY-MM-DD}.md
• Path: {user_dir}/
• Write to {user_dir}/CareerNavigator/artifacts-index.json:

{
  "id": "{uuid}",
  "type": "resume",
  "filename": "{filename}",
  "path": "{user_dir}/{filename}",
  "target_company": "{company}",
  "target_role": "{role}",
  "date_created": "{today}",
  "source": "generated",
  "ats_score": {n},
  "source_units": ["{unit_ids used}"],
  "jd_keywords": ["{keywords matched}"],
  "notes": ""
}

If other artifact files (PDF/DOCX) exist in {user_dir}, run the artifact-saved workflow once after saving so artifacts-index.json stays aligned with disk.

Confirm to the user:

"Saved as {filename}. Run /career-navigator:cover-letter to generate a matching cover letter."

5. Suggest next step

If the user hasn't already logged an application for this role, prompt:

"Want me to add this to your tracker? Just say 'yes' or tell me the application details."