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score-fit

score-fit is a skill for Claude Code from jain777/jobclaw-skills. It costs 52 tokens per session (1,735 once invoked), scanned A, original, MIT.

A job-matching assessment that compares a job description with a stored professional profile and reports an ATS-style match, keywords, strengths, gaps, and an apply-or-skip recommendation. ATS means software employers use to screen applications.

In plain words
What is it for?
Use it to assess pasted, fetched, or saved job descriptions and create structured information that resume-tailoring and cover-letter tools can reuse.
Why use it?
It turns a long job description into a clear decision and shows which requirements your profile supports or misses.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the jobclaw-skills plugin — 20 skills shipped together

not rated 5repo 3mo ago A scan 52 tokens original MIT

Good fit Use it to assess pasted, fetched, or saved job descriptions and create structured information that resume-tailoring and cover-letter tools can reuse.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jain777/jobclaw-skills/score-fit
View source ↗ jain777/jobclaw-skills
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add jain777/jobclaw-skills --skill score-fit
Clone the repo
git clone --depth 1 https://github.com/jain777/jobclaw-skills

Made for: Claude Code.

Or install jobclaw-skills, the plugin that ships this one along with the rest of its 20 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for score-fit

README.md
[![agentmods](https://agentmods.dev/badge/skills/jain777/jobclaw-skills/score-fit/github.svg)](https://agentmods.dev/skills/jain777/jobclaw-skills/score-fit)
Your own site
<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/score-fit"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/score-fit/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for score-fit

Your own site · 80×15
<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/score-fit"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/score-fit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,735 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.01735
Opus 5 $0.00026 $0.00868
Sonnet 5 $0.00010 $0.00347
Haiku 4.5 $0.00005 $0.00173

Measured 12d ago against content hash 99bf19bc175f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

score-fit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/score-fit/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

  1. 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.)

  2. 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).

Read the full file on GitHub · 106 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 106 lines · 52 tokens per session scan A 99bf19bc175f

Subscribe to this mod's changes

score-fit is a skill published in the GitHub repository jain777/jobclaw-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 1,735 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Planning
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