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 surfmind-space/awesome-surfmind --skill job-offer-comparegit clone --depth 1 https://github.com/surfmind-space/awesome-surfmindWrote 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/surfmind-space/awesome-surfmind/job-offer-compare)<a href="https://agentmods.dev/skills/surfmind-space/awesome-surfmind/job-offer-compare"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/job-offer-compare/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/surfmind-space/awesome-surfmind/job-offer-compare"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/job-offer-compare.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00079 | $0.01241 |
| Opus 5 | $0.00039 | $0.00620 |
| Sonnet 5 | $0.00016 | $0.00248 |
| Haiku 4.5 | $0.00008 | $0.00124 |
Grade A, and why
job-offer-compare 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 11d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Offer Compare
Compare and rank multiple job postings or offers as a career decision aid, working from open tabs, pasted descriptions, URLs, selected text, or roles reviewed earlier in the conversation. Score only from the user's stated background and facts visible on the page or from cited search — never invent experience, metrics, salary, titles, or company facts, and preserve names, numbers, dates, titles, links, technologies, and user constraints exactly. Use web search or browser tools when current compensation, company news, layoffs, hiring freezes, or posting liveness matter. Do not submit applications or claim a posting is fake; present the scores and let the user decide. Adapted from santifer/career-ops ofertas mode.
- Gather the postings. If fewer than two are in context, ask the user to paste, open, or name the roles to compare.
- For each posting, confirm it looks active (a real job description and a working apply path) and capture a one-line legitimacy read. Flag any posting that looks expired, redirected, or thin.
- Score every posting 1-5 on each dimension of the matrix below. Mark estimated scores as estimates and label search-derived claims with sources when tools are available.
- Compute a weighted total per posting and rank them.
- Recommend an order to pursue, factoring time-to-offer speed and any hard blockers, and name the single highest-leverage next action.
| Dimension | Weight | 1-5 scale |
|---|---|---|
| Goal alignment | 25% | 5 = exact target role, 1 = unrelated |
| Background match | 15% | 5 = 90%+ match, 1 = under 40% match |
| Level | 15% | 5 = staff+, 4 = senior, 3 = mid-senior, 2 = mid, 1 = junior |
| Estimated compensation | 10% | 5 = top quartile, 1 = below market |
| Growth trajectory | 10% | 5 = clear path to next level, 1 = dead end |
| Remote quality | 5% | 5 = full remote, 1 = onsite only |
| Company reputation | 5% | 5 = top employer, 1 = red flags |
| Tech or tooling modernity | 5% | 5 = current stack, 1 = legacy |
| Time-to-offer speed | 5% | 5 = fast process, 1 = six months or more |
| Cultural signals | 5% | 5 = builder culture, 1 = bureaucratic |
Return these sections: Comparison scorecard — one row per posting with per-dimension scores and the weighted total; Ranking — ordered, with the deciding factors named for the top choice; Legitimacy notes — the one-line read per posting, with any caveats; and Recommendation — which to pursue first and why, plus the next action. If the user provides no background, score what the postings reveal on their own and list the profile details needed for a sharper ranking.
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.
- 11d ago First seen · 58 lines · 79 tokens per session scan A 25e612a685c0
job-offer-compare is a skill published in the GitHub repository surfmind-space/awesome-surfmind (5 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,241 once invoked, about $0.0004 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 2026-08-31.
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