batch-resume
Process multiple job descriptions from the batch_jds folder into tailored application packages, each produced by the native Resume Team, with independent…
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
$ npx -y skills add jananthan30/Resume-Builder --skill job-fit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/job-fitContext 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
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.
Evaluate the configured master resume against an exact job description before any resume work begins.
The user provides the target job description when invoking this skill; treat that text as the exact JD throughout.
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.
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]
================================================================report and digest must be recomputed and bound into any later `resume-team-result/v2` and `resume-team-final-receipt/v2`.
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.
wrong source input before a fresh assessment.
`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.
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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