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/job-fit

Run the deterministic, digest-bound candidate-fit gate that scores the configured master resume against an exact job description before any resume tailoring. Use when the user wants a GO/NO-GO fit check on a JD, asks "should I apply", or before starting resume work, to confirm

From plugin
resume-builder
839 skills4 agents9 commands1 hook
+1
Install
$ npx -y skills add jananthan30/Resume-Builder --skill job-fit --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/job-fit

Context preview

The summary Claude sees to decide when to auto-load this skill.

Run the deterministic, digest-bound candidate-fit gate that scores the configured master resume against an exact job description before any resume tailoring. Use when the user wants a GO/NO-GO fit check on a JD, asks "should I apply", or before starting resume work, to confirm

SKILL.md

job-fit.SKILL.md
name: job-fit
description: Run the deterministic, digest-bound candidate-fit gate that scores the configured master resume against an exact job description before any resume tailoring. Use when the user wants a GO/NO-GO fit check on a JD, asks "should I apply", or before starting resume work, to confirm score >= 70 with zero hard knockouts. Returns PROCEED, REJECTED, or PREFLIGHT FAILED.

Job Fit Pre-Screen — Deterministic GO/NO-GO Gate

Evaluate the configured master resume against an exact job description before any resume work begins.

Job Description

The user provides the target job description when invoking this skill; treat that text as the exact JD throughout.

CANDIDATE-FIT PREFLIGHT (MANDATORY FIRST GATE)

1. Read `config.json` and resolve its exact `master_resume_path`. This configured master is the only resume allowed in the assessment. Never use a previous tailored resume, application resume, or "best match" template. 2. Put the exact job description in a private temporary UTF-8 file. Do not create an application/output directory, resume draft, DOCX, or tracker row. 3. Generate one safe `run_id`, one safe `case_id`, and one strict ISO calendar `as_of_date`, then capture the sole intended machine output from:

`python candidate_fit_preflight.py --resume <configured-master-resume> --job-description <private-exact-JD.txt> --run-id <run_id> --case-id <case_id> --as-of-date <YYYY-MM-DD> --json`

4. Parse only the JSON report. Require `schema_version: "1.0.0"`, `policy_version: "candidate-fit-policy-v3"`, `scorer_version: "evidence-match-v1"`, the exact run/case IDs and date, exact master/JD SHA-256 digests, exact `threshold: 70.0`, all seven named component scores, a boolean `extraction_trustworthy`, `hard_knockouts`, `passed`, and ordered `codes`. Recompute the canonical JSON SHA-256 and display it as `candidate_fit_report_digest`. 5. Proceed status is valid only when the process exits `0`, `score >= 70`, extraction is trustworthy, `hard_knockouts` is empty, `passed` is true, and `codes` is empty. No dimension, recommendation, ATS score, HR score, or user preference can compensate for a failed condition. 6. Exit `1`, any score below 70, or any hard knockout is `REJECTED:CANDIDATE_FIT`. Exit `2` or an unavailable, malformed, non-canonical, stale, or digest-mismatched report is `FAILED:CANDIDATE_FIT_PREFLIGHT`. Both fail closed and authorize no tailoring, role/native-team invocation, output, DOCX, or tracker operation.

Report

Display the exact source-bound decision clearly:

================================================================
  CANDIDATE FIT: XX.X/100 — [PROCEED | REJECTED | PREFLIGHT FAILED]
================================================================

  Source: configured master_resume_path
  Policy: candidate-fit-policy-v3 | Threshold: 70.0
  Master digest: [SHA-256]
  JD digest: [SHA-256]
  Report digest: [candidate_fit_report_digest]

  HARD KNOCKOUTS:
  [None, or exact category / requirement / candidate-has summary]

  COMPONENTS:
  Experience Match:     XX/100
  Skills Match:         XX/100
  Title Alignment:      XX/100
  Domain Match:         XX/100
  Education Match:      XX/100
  Certification Match:  XX/100
  Seniority Match:      XX/100

  CODES: [none | UNTRUSTWORTHY_EXTRACTION | ELIGIBILITY_FAILED |
          SCORE_BELOW_THRESHOLD]

  VERDICT:
  [Clear next action]
================================================================

Fixed recommendation policy

  • `70–100`, zero hard knockouts, trustworthy extraction: `PROCEED`. The same

report and digest must be recomputed and bound into any later `resume-team-result/v2` and `resume-team-final-receipt/v2`.

  • `60–69`: `REJECTED:CANDIDATE_FIT` — blocked for manual reconsideration. The

system must not automatically tailor or offer a bypass. Reconsideration means reviewing the target or adding genuine evidence to the master resume, then running a fresh assessment; it is not permission to override the threshold.

  • Below `60`: `REJECTED:CANDIDATE_FIT` — target a closer role or correct genuinely

wrong source input before a fresh assessment.

  • Any hard knockout: `REJECTED:CANDIDATE_FIT` regardless of score.
  • Untrustworthy extraction or any invalid report:

`FAILED:CANDIDATE_FIT_PREFLIGHT`; fix the input/tooling and rerun from scratch.

ATS and HR baseline scoring may be shown later as advisory diagnostics only after candidate fit passes. They are not this gate, cannot raise or lower its decision, and cannot authorize resume development.

Read more
Ships withresume-builder

Every AI resume tool promises to "beat the ATS." This one has a harder rule: it never invents experience — and when a job is a genuine mismatch, it declines to tailor at all and tells you why.

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