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.

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

It turns a long job description into a clear decision and shows which requirements your profile supports or misses.

score-fit — Skill for Claude Code by 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.

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.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00052	$0.01735
Opus 5.5	$0.00021	$0.00694
Sonnet 5.5	$0.00010	$0.00347
Haiku 4.5	$0.00005	$0.00173

Measured 2mo ago against content hash 99bf19bc175f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 2mo 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

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.
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)

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.)
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.

reference/score-schema.md 3.5 KB

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.

2mo 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 (7 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 1,735 once invoked, about $0.0002 per session on Opus 5.5. 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

score-fit next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
score-fitjain777/jobclaw-skills	Skill	7	5mo ago	MIT	A	52	Claude Code
imagegenopenai/codex	Skill	135k	today	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	100k	today	Apache-2.0	A	19	Claude Code
browser-automationopenclaw/openclaw	Skill	394k	today	MIT	A	36	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	316k	7d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code

score-fit

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

score-fit

Inputs

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

What ships with it

What this file has done since we first saw it

score-fit compared with skills from other repositories