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 skills add suxrobGM/jobpilot --skill cover-lettergit clone --depth 1 https://github.com/suxrobGM/jobpilotWrote 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/skills/suxrobgm/jobpilot/cover-letter)<a href="https://agentmods.dev/skills/suxrobgm/jobpilot/cover-letter"><img src="https://agentmods.dev/badge/skills/suxrobgm/jobpilot/cover-letter/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/skills/suxrobgm/jobpilot/cover-letter"><img src="https://agentmods.dev/badge/skills/suxrobgm/jobpilot/cover-letter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 81 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00031 | $0.01623 |
| Opus 5 | $0.00015 | $0.00812 |
| Sonnet 5 | $0.00006 | $0.00325 |
| Haiku 4.5 | $0.00003 | $0.00162 |
Grade A, and why
cover-letter 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 2d 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.
curl -fsS -H "authorization: Bearer $JOBPILOT_API_TOKEN" "$JOBPILOT_API/api/cover-letters?page=1&pageSize=5" | jq -r '.items[].id' How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cover Letter Generator
Write a short, tailored cover letter connecting the candidate's resume to a specific role.
Setup
Follow ../_shared/setup.md to load profile and resume. That structured resume is what you write from - experience, projects, skills, education, publications, awards, certifications and any custom sections are all in it. Don't also Read the source PDF: it's the same content again at several times the size, and inside an apply flow that cost is paid per job.
Step 1: Analyze the JD
From the argument, identify: company + what they do, role title and level, key responsibilities, required/preferred qualifications, tech stack and domain, culture cues.
Step 2: Read Your Last 5 Letters
Do not skip. Letters that are individually fine and collectively identical are what reads as AI - not word choice.
curl -fsS -H "authorization: Bearer $JOBPILOT_API_TOKEN" "$JOBPILOT_API/api/cover-letters?page=1&pageSize=5" | jq -r '.items[].id'
The list is metadata only, so GET /api/cover-letters/<id> each for the body. No history (first letter, or the call fails) → Step 3.
Take three things from each and drop the rest: which experience it led with, its metric phrasings, and its closing sentence. Those are what rules 14-16 compare against; keeping five whole letters in context buys nothing beyond them.
Step 3: Select Relevant Experience
From the resume, pick the most relevant: 2–3 work experiences, 2–3 projects, research (if AI/ML/CV), education (if relevant to level).
Apply rules 14-16 here, against Step 2's letters. Rotating the lead is a selection decision - make it before drafting, not by patching a draft that already opened with the usual paragraph.
Step 4: Write
Header - values strictly from user.*, never from the resume file (resumes carry stale addresses). Omit any line whose fields are empty:
[Full Name]
[City, State] | [Phone] | [Email]
[LinkedIn] | [GitHub] | [Website]
Body - 150–250 words, 3–4 paragraphs of uneven length.
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.
- 2d ago Changed 7bf997ab4907
- 6d ago Changed fc09be33e86a
- 11d ago First seen · 113 lines · 31 tokens per session scan A c143017d9982
cover-letter is a skill published in the GitHub repository suxrobGM/jobpilot (67 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 1,623 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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