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 tam159/next-role --skill hiring-recongit clone --depth 1 https://github.com/tam159/next-roleWrote 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/tam159/next-role/hiring-recon)<a href="https://agentmods.dev/skills/tam159/next-role/hiring-recon"><img src="https://agentmods.dev/badge/skills/tam159/next-role/hiring-recon/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/tam159/next-role/hiring-recon"><img src="https://agentmods.dev/badge/skills/tam159/next-role/hiring-recon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00063 | $0.01109 |
| Opus 5 | $0.00032 | $0.00554 |
| Sonnet 5 | $0.00013 | $0.00222 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
hiring-recon 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 10d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hiring Recon
Pre-interview reconnaissance. After a CV and JD are processed, gather public intelligence on the company and the role and produce a match analysis the candidate can use to prepare.
Inputs
The caller passes exact filesystem paths in the task description:
resume_path— the candidate's processed resume (markdown)jd_path— the processed job description (markdown)intake_path— optional intake notes (may not exist)output_path— where to write the final report
Read every input file in full with read_file(path, limit=1000). Do not skim. Note the candidate's location (from the resume) and the JD's location up front — both matter for salary calibration.
Tools
web_search(query, max_results=5, topic="general"|"news"|"finance")— primary researchweb_extract(urls, content_format="markdown")— pull a single high-value page (careers, About, press release, levels.fyi or local equivalent) when a search snippet is too thin- filesystem tools (
read_file,write_file,edit_file,ls,glob,grep)
Plan 3-5 targeted searches, then optionally one or two web_extract calls. Quality over quantity.
Research axes
- Company snapshot — size, stage (startup / scale-up / public), business model (B2B vs B2C, product vs services/outsourcing), main products, recent news.
- Financial & hiring signals — funding rounds, revenue/profitability hints, layoffs, headcount trend, recent leadership changes. Use
topic="news"ortopic="finance"for these. - Reputation & culture — Glassdoor patterns: report repeated themes in 1-2 star reviews (overwork, attrition, leadership) rather than just the average rating. Tenure signals, controversies.
- Hiring team — if discoverable, who the hiring manager / team lead is and their background. Omit this section entirely if no signal.
- Role market context — typical skills expected in this role family; JD-language red flags using the linguistic taxonomy ("wear many hats" → workload risk; "rockstar/ninja" → culture risk; "competitive salary" → likely below market). Include a Salary range bullet bracketed by location:
Senior <Role>, <region>: ~$X-$Y per <source>. If candidate location and JD location differ, list both. Never quote a generic global number. - Match analysis — given the resume vs. the JD vs. company priorities, strengths to emphasize, gaps to address with adjacent experience. 3-5 bullets each.
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.
- 10d ago First seen · 82 lines · 63 tokens per session scan A 28b85aad17cf
hiring-recon is a skill published in the GitHub repository tam159/next-role (50 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 1,109 once invoked, about $0.0003 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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