A command that gives each newly collected job posting a preliminary fit score and creates a ranked shortlist. It uses the job text and the candidate profile, while leaving detailed evaluation and company research to a later step.

Use it to rank all new jobs, filter rankings by an area such as data science, refresh scores after changing your profile, or choose a shortlist of a specific size.

It removes the need to review every new listing with the same level of attention. The shortlist helps you decide which applications deserve deeper work first.

rank — Command for Claude Code by ishandutta2007

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.

Clone the repo

git clone --depth 1 https://github.com/ishandutta2007/Awesome-AI-Job-Hunting

Made for: Claude Code.

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/commands/ishandutta2007/awesome-ai-job-hunting/rank/github.svg)](https://agentmods.dev/commands/ishandutta2007/awesome-ai-job-hunting/rank)

Your own site

<a href="https://agentmods.dev/commands/ishandutta2007/awesome-ai-job-hunting/rank"><img src="https://agentmods.dev/badge/commands/ishandutta2007/awesome-ai-job-hunting/rank/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/commands/ishandutta2007/awesome-ai-job-hunting/rank"><img src="https://agentmods.dev/badge/commands/ishandutta2007/awesome-ai-job-hunting/rank.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 3,459 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod 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	$0.00000	$0.03459
Opus 5	$0.00000	$0.01729
Sonnet 5	$0.00000	$0.00692
Haiku 4.5	$0.00000	$0.00346

Measured 12d ago against content hash a33c698efc4d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

rank scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- **Before marking anything `expired`, the agent must exhaust the escalation order** in `.claude/skills/job-application-assistant/09-web-research.md`: a `WebFetch` 403 is a rejected *client*, not a missing page, and retr

Origin

This is a copy

100% identical to rank — 79 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/commands/rank.md · 148 lines

How it starts

The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/rank - Triage Scraped Jobs into a Ranked Shortlist

You are batch-scoring the jobs that /scrape has collected, so the user can decide where to spend /apply effort. /scrape finds and dedupes postings; /apply evaluates one at a time in depth. /rank is the bridge: it scores every new posting against the fit framework and returns a ranked shortlist.

/rank produces triage scores, not final evaluations. It scores from the posting text and the candidate profile only - no company research, no reviewer agent. /apply's Step 1 evaluation (which adds company research) remains authoritative and always re-runs when the user applies.

Follow these steps in order.

Step 0: Parse Input

$ARGUMENTS may contain:

Nothing → rank all jobs with status new in job_scraper/seen_jobs.json
A focus area (e.g. /rank data science) → rank only jobs whose title or stored fit-notes match the focus
--all → re-rank every job that has not been applied to, including previously ranked ones (useful after the profile changes)
--top <N> → shortlist size (default 5)

Step 1: Load State

Read job_scraper/seen_jobs.json. If the file is missing or has no entries, tell the user to run /scrape first and stop.
Read job_search_tracker.csv. Build the exclusion set: any company+role already in the tracker is out of scope regardless of flags - it has been applied to or consciously tracked.
Select candidates: entries with status new (or entries of any status with --all), minus the exclusion set, filtered by the focus area if one was given.
If no candidates remain, say so ("Nothing new to rank - run /scrape to find fresh postings") and stop.
Read the scoring framework and profile once:
- .claude/skills/job-application-assistant/04-job-evaluation.md
- .claude/skills/job-application-assistant/01-candidate-profile.md

State how many jobs will be ranked before proceeding.

Step 2: Batch-Fetch and Score

Dispatch parallel general-purpose agents via the Agent tool, ~5 jobs per agent (a single agent is fine for ≤5 jobs). Token-efficiency rules, consistent with /apply:

Read the full file on GitHub · 148 lines

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.

12d ago First seen · 148 lines · 0 tokens per session scan A a33c698efc4d

Subscribe to this mod's changes

rank is a command published in the GitHub repository ishandutta2007/Awesome-AI-Job-Hunting (3 stars, last pushed 17d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,459 tokens. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to rank, differing in 79 lines, and is treated as a copy.

awesome-list curated-list

rank

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Makes network callslowCapability

This is a copy

How it starts

/rank - Triage Scraped Jobs into a Ranked Shortlist

Step 0: Parse Input

Step 1: Load State

Step 2: Batch-Fetch and Score

What this file has done since we first saw it

Other commands, from other repositories

review-issue

new-sdk-app

analyze-codebase

tdd

review

test