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/treymorgan/jobsearch-apply-mcp/applygit clone --depth 1 https://github.com/treymorgan/jobsearch-apply-mcpWhat 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 | $0.00000 | $0.07943 |
| Opus 5 | $0.00000 | $0.03972 |
| Sonnet 5 | $0.00000 | $0.01589 |
| Haiku 4.5 | $0.00000 | $0.00794 |
Grade A, and why
apply 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 today.
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.
- **If the fetch returns HTTP 403, or the content is a login wall or an unrelated listing page, do not give up and do not draft from the title.** Follow the escalation order in `.claude/skills/job-application-assistant/0 This is a copy
86% identical to apply — 52 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.
How it starts
The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/apply - Drafter-Reviewer Job Application Workflow
You are orchestrating a two-agent job application workflow. The job posting is provided below as $ARGUMENTS (either a URL or pasted text).
Follow these steps exactly in order. Do not skip steps.
Standing rule — write new facts back to the profile. If the user confirms, corrects or supplies a fact that is not already in 01-candidate-profile.md — a metric, a project detail, a skill, a scope correction — update that file in the same turn. Do not leave it living only in the conversation or in a draft.
This is not bookkeeping. A fact that exists only in chat will be treated as unsupported by a later session and stripped from drafts as a fabrication. Anything absent from the sources does not exist as far as future drafting is concerned, and the loss is silent — a real achievement quietly disappears from every subsequent CV.
This rule is the input side of the Step 3 Factual Grounding Audit, not a competitor to it. The audit is deliberately strict: an ungrounded claim is removed, and it cannot tell a fabrication from a real fact the user stated out loud last week. That strictness is correct, and it is exactly why confirmed facts have to reach the sources in the same turn they surface. Write to 01-candidate-profile.md specifically — it is one of the audit's three sources, so a fact recorded there is grounded on the next run. Adding a fact to 01 that CLAUDE.md and the master CV simply do not mention is an absence, not a contradiction, and does not trip the audit's profile-consistency warning; if the new fact corrects something either of those states, fix it there too rather than leaving the two sources disagreeing.
Token-efficiency rules for this workflow:
- Never re-Read a file whose contents are already in your context from an earlier step. If you read it in Step 1, it is still available in Step 2.
- When dispatching the reviewer agent, pass draft content inline in the agent prompt rather than asking the agent to Read files you already have in memory.
- Run the full verification checklist exactly once, at the end (Step 6). The reviewer focuses on content critique, not verification.
- Step 5 (compile and inspect PDFs) is mandatory and non-skippable — page-break decisions are unpredictable, and source files that look fine often produce broken PDFs (orphaned entry titles, cover letters spilling to page 2, bullet fonts mismatching).
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.
- today First seen · 381 lines · 0 tokens per session scan A 12347622b543
apply is a command published in the GitHub repository treymorgan/jobsearch-apply-mcp (0 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 7,943 tokens. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to apply, differing in 52 lines, and is treated as a copy.
Other commands, from other repositories
compile-today
Compiles today's CVs, runs the ATS check, and archives (doesn't touch the tracker).
interview-prep
Read workflows/interview-prep.md, profile/candidate.yaml, rules/interview-roteiro.md + rules/job-evaluation.md (to re-fetch the job description) and rules/cv-rules.md (for the overlap logic reused when picking the project). Execute the steps described there for the company received in $ARGUMENTS.
apply-batch
Read workflows/apply-batch.md, profile/candidate.yaml and rules/cv-rules.md.
confirm
Read workflows/confirm.md and profile/candidate.yaml (the tracker.backend field decides whether registration goes to Notion, CSV, or none). Execute the steps described there for the indices/names received in $ARGUMENTS.
daily
Read workflows/daily.md and profile/candidate.yaml (run python3 scripts/validateprofile.py first to validate). Execute the steps described there, using rules/job-evaluation.md for the fit score.
quick-eval
Quick job evaluation. Paste a JD and get a score plus one-paragraph summary. Faster than a full evaluate. Use when someone says 'quick eval', 'quick score', or 'just give me a number'.