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.
npx agentmods add commands/jananthan30/resume-builder/batch-resumegit clone --depth 1 https://github.com/jananthan30/Resume-BuilderWrote 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/jananthan30/resume-builder/batch-resume)<a href="https://agentmods.dev/commands/jananthan30/resume-builder/batch-resume"><img src="https://agentmods.dev/badge/commands/jananthan30/resume-builder/batch-resume.svg" alt="Measured on agentmods" 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 | $0.00013 | $0.05036 |
| Opus 5 | $0.00006 | $0.02518 |
| Sonnet 5 | $0.00003 | $0.01007 |
| Haiku 4.5 | $0.00001 | $0.00504 |
Grade B, and why
batch-resume scanned grade B with 2 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 6d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl -s -X POST http://localhost:8100/score/both -H "Content-Type: application/json" -d "{\"resume_path\": \"applications/{Company} - {Job Title}/resume.md\", \"jd_path\": \"applications/{Company} - {Job Title}/job_descr Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:8100/health How it starts
The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Batch Resume Builder — Native Resume Teams
Process multiple job descriptions with concurrent read/scoring work. Each resume is produced by the native four-role team; final DOCX and tracker mutations remain ordered and coordinator-owned.
Arguments
$ARGUMENTS
Instructions
You are the team lead for a batch resume processing operation. Execute the following steps:
STEP 1: DISCOVER JDs (READ-ONLY)
Scan the batch_jds/ folder for .txt files:
Use file search (`rg --files batch_jds` or `find`) for `batch_jds/*.txt`
Each file should be named: {Company} - {Job Title}.txt
Parse the filename to extract:
- Company: Everything before
- - Job Title: Everything after
-(without.txt)
If no files found, tell the user to add JD text files to batch_jds/ and explain the naming format.
Display a numbered list of all JDs found and confirm with the user before proceeding.
STEP 2: CANDIDATE-FIT PREFLIGHT (MANDATORY FIRST GATE PER JD)
Resolve and read master_resume_path from config.json once. It is the sole base
and factual source for every job; never inspect, screen, or substitute an existing
tailored application resume. For each exact JD, independently generate a safe
run_id, safe case_id, and strict ISO as_of_date, then run the deterministic
machine preflight before starting a scorer, role/native team, output directory,
resume draft, DOCX, or tracker operation for that JD:
python candidate_fit_preflight.py --resume <configured-master-resume> --job-description <exact-JD-file> --run-id <run_id> --case-id <case_id> --as-of-date <YYYY-MM-DD> --json
Require exit 0 and a valid candidate-fit-policy-v3 report bound to that JD,
master, date, and IDs. Canonically hash it as candidate_fit_report_digest.
A passing report has exact threshold 70.0, score at least 70, trustworthy
extraction, zero hard knockouts, passed: true, and no codes. Exit 1, any score
below 70 (including 60–69), or any hard knockout is
REJECTED:CANDIDATE_FIT. Create nothing for that JD, record the rejection in the
in-memory/private batch report, and continue screening the remaining JDs. Exit 2
or an unavailable, malformed, stale, non-canonical, or digest-mismatched report is
FAILED:CANDIDATE_FIT_PREFLIGHT; fail that JD closed, create nothing, and continue
the remaining independent JDs. There is no automatic or manual workflow bypass.
ATS/HR scores are advisory and cannot override this gate.
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.
- 6d ago First seen · 476 lines · 13 tokens per session scan B b4dc1b79d0e2
batch-resume is a command published in the GitHub repository jananthan30/Resume-Builder (81 stars, last pushed 22d ago), licensed MIT. It adds 13 tokens to every session and 5,036 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
tailor-cv
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triage-inbox
Sort application mail into the freehire tracker and drain the link queues.
job-search
Search live IT vacancies on freehire and shortlist the ones worth applying to.
market-fit
Score a stack against live vacancy demand and name the gaps worth closing.
track-applications
Review where every application stands and bring the tracker up to date.
start
Onboard with Rebound — capture your base resume and build your private profile (skills, differentiators, and situation).