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/cover-lettergit clone --depth 1 https://github.com/jananthan30/Resume-BuilderWhat 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 | $0.00017 | $0.01149 |
| Opus 5 | $0.00009 | $0.00575 |
| Sonnet 5 | $0.00003 | $0.00230 |
| Haiku 4.5 | $0.00002 | $0.00115 |
Grade A, and why
cover-letter 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 yesterday.
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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Cover Letter Only
Create a compelling one-page cover letter for a job application.
Job Description
$ARGUMENTS
Instructions
You are the cover-letter coordinator. The user has provided a job description above.
Your task:
Phase 1: Setup
-
Extract company name and job title from the job description
-
Search for a similar existing resume in the
applications/folder to understand what was already tailored:- List all subfolders in
applications/ - Compare folder job titles against the NEW job description's title and requirements
- If a similar resume is found: Read that resume to understand the applicant's tailored background for this type of role
- If NO similar resume is found: Read the master resume (path from
config.json→master_resume_path, or glob for*MASTER*RESUME*.md) to understand the applicant's background - Always also read the master resume for canonical details
- Treat an existing tailored resume as eligible evidence only if
resume_integrity_audit.py --config config.json --tailored <resume>exits 0. Otherwise use the master resume. - If this request also requires creating or changing a resume, run a complete native
resume-team/v2workflow incommands/resume-team.mdunder a freshrun_id; never draft or patch a resume inside this command. After that run authorizes an exact resume digest, cover-letter work must not alter the resume bytes or reuse stale authorization.
- List all subfolders in
-
Delegate JD analysis to the read-only native
resume-researcherusing only the job description. Validate itsresume-team-handoff/v1response. Use its rubric only to choose relevant, already-supported experiences; JD requirements are not evidence that the candidate has a skill. -
Create output folder at
applications/{CompanyName} - {JobTitle}/(if not exists) -
Save the job description as
job_description.txt(if not exists)
Phase 2: Cover Letter Generation
- Write a persuasive one-page cover letter that sounds human — brief, specific, varied rhythm. Follow
commands/writing-coach.mdRules 0, 11–16 for letters.
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.
- yesterday First seen · 95 lines · 17 tokens per session scan A fbe5d80c8955
cover-letter is a command published in the GitHub repository jananthan30/Resume-Builder (76 stars, last pushed 17d ago), licensed MIT. It adds 17 tokens to every session and 1,149 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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outcome
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