score-fit

score-fit

Judge a job against the user's profile the way a good recruiter + ATS would, and give a decision — not just a number.

Inputs

1. The profile: read profile/master-profile.md.
   • Missing → offer a degraded one-shot mode: score against a resume/profile snippet the user pastes now, and set profile_missing: true in the sidecar. Suggest /build-profile for next time.
   • Stale → if fields the score depends on are marked [VERIFY], warn in the output and set profile_stale: true. Never credit [VERIFY] facts toward must-haves.
2. The job: a pasted description, a URL to fetch, or an entry from jobs/found-<date>.json. Known-info gate (RULES §6): if no job is given inline, read jobs/current.json (the job under work) and use its jd_text/url — do not re-ask for the JD if it's already captured. After resolving the job, write/refresh jobs/current.json ({company, role, url, job_id, jd_text, region, source, captured_at}) so tailor-resume / write-cover-letter / answer-application-questions reuse it without asking again. Only ask the user for a job if none is supplied and current.json is absent.

Method (think like an ATS, then like a hiring manager)

0. Apply work-authorization logic first. Determine the job's country and resolve the applicant's work_auth per ../../knowledge/work-authorization.md — it uses the job's region pack, not the applicant's, and returns one of NON-ISSUE / MINOR-NOTE / MAJOR-FILTER / DISQUALIFIER. Record it as work_auth_verdict. (E.g. India applicant + India job → NON-ISSUE: do not raise or penalize; weight notice-period/CTC fit instead. The same applicant + a US "no sponsorship" job → MAJOR-FILTER.)

1. Extract the job's requirements: hard requirements (must-haves), preferred (nice-to-haves), key skills/keywords, seniority, domain, location/remote, comp if stated.

   • AI/ML roles: if the role is AI/ML, detect its archetype per ../../knowledge/ai-roles.md (AI Platform/LLMOps · Agentic · Technical AI PM · Solutions Architect · Forward-Deployed · Transformation). Weight the domain and must_haves sub-scores toward that archetype's proof points, note it in strengths/gaps, and record archetype in the sidecar. Distinguish genuine AI roles from "AI" used only as a buzzword (title filter in ai-roles.md).

2. Grade each requirement as met / partial / missing, citing profile evidence:

   • met — clear evidence in the resume; partial — adjacent/transferable experience, or only part of a compound requirement is shown; missing — no evidence. Don't credit skills not shown.
   • Match semantically, not literally (the shared engine — ../../knowledge/relevance.md): credit a skill demonstrated under a different name (a "Visual AI agent" shows computer vision; "ran paid campaigns" shows performance marketing; "RAG" ≈ retrieval-augmented). Literal keyword absence is not the same as missing evidence. But never credit [VERIFY] facts — not even as transferable.
   • "X OR Y" requirements — satisfied if the profile has either. Never penalize for lacking the other half of an OR.
   • Ambiguous / underspecified JDs — score conservatively, note the ambiguity in gaps, and don't inflate must-haves. Lower confidence rather than guessing high.
   • Mark is_dealbreaker: true for must-haves whose absence alone should drive a Skip.

3. Keyword coverage: list keywords present in the profile (matched_keywords) vs. missing ones an ATS would screen on (missing_keywords). Keep missing_keywords to terms the resume could honestly incorporate — this list feeds tailor-resume.

4. Score 0–100 using this weighting (adjust slightly per role) — these are the subscores:

   • Must-have requirements met — 45
   • Seniority & scope match — 20
   • Domain/industry relevance — 15
   • Keyword/skills coverage — 15
   • Location / remote / work-auth fit — 5

   Then subtract itemized penalties for hard-constraint violations (onsite when remote-only; sponsorship not offered when needed per §0; comp clearly below min_base_salary). The final score = sum(subscores) + sum(penalties), clamped to 0–100. (Subscores still sum to 100 — the factor weights mirror ../../knowledge/relevance.md, so this score stays directionally consistent with find-jobs fit_rank.)

5. Goal alignment (directional — not a subscore). Read context.career_goal to judge whether this role advances the candidate's stated direction; set goal_alignment ∈ strong|neutral|weak. It breaks ties / nudges the recommendation (e.g. a borderline 70 that strongly serves the goal → "apply"; one that pulls away → "apply_if_tailored"). It is never a numeric subscore (preserves the 100-point sum) and is never quoted, paraphrased, or echoed into any output (RULES.md §2) — it directs the verdict, it does not appear in it.

Output

Human-readable (always)

• Score: NN/100 + one-line verdict.
• Recommendation: Apply / Apply if tailored / Skip — with the single biggest reason.
• Strengths (3–5 bullets) — citing profile evidence.
• Gaps / risks (3–5 bullets) — missing must-haves or weak areas; note dealbreakers vs. addressable.
• Missing keywords — comma list the resume should incorporate (feeds tailor-resume).
• If recommending Apply, suggest /tailor-resume for this job.

Structured sidecar (gated)

When the job has an id (from jobs/found-<date>.json) or score-fit runs inside a pipeline, also write scores/<job-id>.score.json per reference/score-schema.md (subscores must sum to the pre-penalty total). For one-off pasted JDs, skip the file unless asked.

Be honest — a low score is more useful than a flattering one. No emoji.

Next steps

Apply / Apply-if-tailored → /tailor-resume for this job (or /apply-to-job to assemble the whole package). Skip → move to the next job.