agentleFS
QaliAIQaliAI/ai-job-agent/skills/setup-profile/SKILL.md

setup-profile

Skill0 starsChanged 3 months agoMITView on GitHub

Create or update the candidate's canonical factual career profile from their resume and answers.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read user's pasted resume text or file in candidate/.

    line 15
  2. Distinctive:

    Do NOT invent metrics or embellish scope.

    line 17
  3. Distinctive:

    Use opportunity tracks when the candidate plausibly spans distinct role families (for example AI consulting + product building + growth).

    line 28
  4. Distinctive:

    Keep every track grounded in actual evidence from the profile.

    line 29
  5. Distinctive:

    Do not create a track merely to chase a posting the candidate cannot support.

    line 30
  6. Very specific:

    Run python scripts/doctor.py to confirm profile files are valid and protected by .gitignore.

    line 33

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

setup-profile

Transform the candidate's raw résumé and search preferences into a durable, local-first source of truth.

Workflow

  1. Ingest Existing Résumé:
    • Read user's pasted resume text or file in candidate/.
    • Extract strictly verified facts: work history, company names, titles, dates, education, and technical competencies.
    • Do NOT invent metrics or embellish scope.
  2. Establish Files in candidate/:
    • MASTER_PROFILE.md: Complete chronological career history, contact info, summary, and honest framing notes.
    • SEARCH_PREFERENCES.md: Target roles, work mode (remote/hybrid/onsite), locations, compensation minimum, and company exclusions.
    • VERIFIED_ACHIEVEMENTS.md: Bullet points with verified metrics and outcomes.
    • CAREER_STORIES.md: STAR-format narratives of key technical challenges.
    • skills.json: Categorized JSON taxonomy of proven vs transferable skills.
    • OPPORTUNITY_TRACKS.json (optional): Multiple legitimate career/revenue lanes, each with its own search queries and fit signals. Start from templates/OPPORTUNITY_TRACKS_TEMPLATE.json.
  3. Configure Multi-Track Search When Needed:
    • Use opportunity tracks when the candidate plausibly spans distinct role families (for example AI consulting + product building + growth).
    • Keep every track grounded in actual evidence from the profile.
    • Do not create a track merely to chase a posting the candidate cannot support.
  4. Verify Integrity:
    • Run python scripts/doctor.py to confirm profile files are valid and protected by .gitignore.

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