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 neonwatty/job-apply-plugin --skill job-preferencesgit clone --depth 1 https://github.com/neonwatty/job-apply-pluginWrote 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/neonwatty/job-apply-plugin/job-preferences)<a href="https://agentmods.dev/skills/neonwatty/job-apply-plugin/job-preferences"><img src="https://agentmods.dev/badge/skills/neonwatty/job-apply-plugin/job-preferences/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/neonwatty/job-apply-plugin/job-preferences"><img src="https://agentmods.dev/badge/skills/neonwatty/job-apply-plugin/job-preferences.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 Rogue Agent · line 17 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00028 | $0.01004 |
| Opus 5 | $0.00014 | $0.00502 |
| Sonnet 5 | $0.00006 | $0.00201 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
job-preferences scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Preferences
A Codex and Claude Code skill for managing persistent job search preferences. Set your target titles, salary floor, remote preference, and exclusion filters once; the bundled job-search skill reuses them automatically.
Workflow
Step 1: Load Profile
Follow the bundled answer-memory skill ($job-apply:answer-memory in Codex; /job-apply:answer-memory in Claude Code). Resolve <plugin-root> as that skill directs, run python3 "<plugin-root>/scripts/job-apply-store.py" init, then load saved preferences with preferences-get. Never read or write persistent Job Apply files directly.
Step 2: Check for Existing Preferences
Use the JSON object returned by preferences-get.
If preferences exist, display them:
Your current job search preferences:
- Target titles: Staff AI Engineer, Principal ML Engineer
- Min base salary: $250K
- Remote preference: Remote only
- Exclude patterns: junior, associate, intern, entry level
- Default time range: last week
Would you like to update any of these?
Then wait for the user. If they say no, stop. If they want to update, ask only about the fields they want to change using the host's structured question surface when available, or concise direct questions otherwise.
If no preferences exist, run the full Q&A below.
Step 3: Collect Preferences (full Q&A)
Use the host's structured question surface when available; otherwise ask concise direct questions. Ask no more than 4 questions at a time.
Question batch 1:
-
Target titles (multi-select + custom)
- Header: "Titles"
- Question: "Which job titles are you targeting?"
- Options: Staff AI Engineer, Principal ML Engineer, Director of AI, Head of ML
- Multi-select: true
- The user can add custom titles via "Other"
-
Min base salary
- Header: "Salary"
- Question: "What is your minimum base salary?"
- Options: $200K, $250K, $300K
- Multi-select: false
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.
- 12d ago First seen · 117 lines · 28 tokens per session scan A 70e238c909b7
job-preferences is a skill published in the GitHub repository neonwatty/job-apply-plugin (107 stars, last pushed today), licensed MIT. It adds 28 tokens to every session and 1,004 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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