| name | job-search |
| description | Find job ads on the web and match them against a resume (ResumeJson). Use this when the user wants to search for jobs that fit their resume, capture a specific job posting from a URL into structured data, or check how well their resume matches a role. Triggers on: "find jobs for my resume", "search for jobs", "job postings for me", "does my resume fit this job", "match my resume to this posting", "grab this job listing", or any reference to job ads / job search / job matching. All steps run on the current Claude session using your WebSearch / WebFetch tools — no OpenAI, no API key.
|
job-search
Find relevant job ads on the web and match them against a ResumeJson. You,
Claude, do the searching and reading in this session with your WebSearch and
WebFetch tools — there is no external model and no API key. A small TypeScript
toolbelt CLI validates the structured output you produce.
The toolbelt CLI
npm run resumejson_claude -- <command> [args]
| Command | Purpose |
|---|
schema <name> | Print the JSON Schema for the contract you must produce |
validate -i <file> --schema <name> | Zod-validate; exits non-zero and lists path: message problems |
Schema names: job-posting, job-match. A resume is a ResumeJson (produced by
the resume-convert skill — run that first if the user only has a PDF/Markdown).
Ground rules (important)
- Never fabricate a posting. Record only what the page actually states. If you
cannot fetch or read a page, say so — do not invent a listing.
- Always keep the
source and url. Every job-posting must be traceable to
the page you read.
- Web access is best-effort. Many boards (LinkedIn feeds, Indeed apply-walls)
require login or run JavaScript and won't be fetchable. Prefer web-search
results and publicly readable postings (company career pages, public listings);
degrade gracefully and tell the user what you couldn't read.
- Match honestly. In
job-match, base matchedRequirements on evidence in the
resume; put unevidenced items in missingRequirements. tailoringTips must be
truthful reframings, never invented experience.
The loop for every structured step
- Fetch the contract:
schema job-posting (or schema job-match).
- Produce the object from what you read.
- Write it to a file.
validate -i <file> --schema <name>; fix reported problems; re-validate.- Summarize for the user.
Capabilities
find_jobs — resume (or criteria) → many job-posting
- Derive search terms from the
ResumeJson (target basics.label, top skills,
seniority) or from criteria the user gives (role, location, remote).
WebSearch for current openings; pick the most relevant, publicly readable
results.WebFetch each and extract it into a job-posting (one file per posting,
e.g. jobs/<company>-<slug>.job.json), validating each.- Present a short ranked list (title · company · location · why it fits).
from_url — a job URL → job-posting
WebFetch the URL, extract into a job-posting, validate. If the page is
login/JS-gated and unreadable, tell the user rather than guessing.
match — resume + job-posting → job-match
Compare a ResumeJson against a job-posting: which requirements/keywords the
resume satisfies vs. misses, overall fit (0–100) and a verdict, plus concrete
tailoringTips. Write a job-match file and validate it, then summarize
(verdict, fit, top 2–3 gaps).
Shortlist workflow
When the user asks to "find jobs that fit my resume":
find_jobs resume.json -> jobs/*.job.json (validated)
match resume.json + each job.json -> jobs/*.match.json (validated)
rank by overallFit and present a shortlist (verdict, fit, gaps, source URL)
Notes
- Reuses the
ResumeJson contract from the resume-convert skill; ATS scoring /
rewriting lives in resume-ats. A natural chain: match reveals gaps →
resume-ats ats_optimize tailors the resume for the target posting.
- No credentials are ever needed — the reasoning and web reads are you; the
validation is local TypeScript.
