Borrowing it
Nothing to install: this file belongs to cypggs/ai-job-search-cn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cypggs/ai-job-search-cn/master/.claude/commands/rank.mdgit clone --depth 1 https://github.com/cypggs/ai-job-search-cnWrote 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.
[](https://agentmods.dev/commands/cypggs/ai-job-search-cn/rank)<a href="https://agentmods.dev/commands/cypggs/ai-job-search-cn/rank"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/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.
<a href="https://agentmods.dev/commands/cypggs/ai-job-search-cn/rank"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/rank.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.01793 |
| Opus 5 | $0.00000 | $0.00897 |
| Sonnet 5 | $0.00000 | $0.00359 |
| Haiku 4.5 | $0.00000 | $0.00179 |
Grade A, and why
rank 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 128 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
newinjob_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/scrapefirst 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 all non-applied entries 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:
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.
- 13d ago First seen · 128 lines · 0 tokens per session scan A eeecc5e05240
rank is a command published in the GitHub repository cypggs/ai-job-search-cn (63 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,793 tokens. 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 2026-08-30.
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