hiring-recon

hiring-recon is a skill for Claude Code, Codex from tam159/next-role. It costs 63 tokens per session (1,109 once invoked), scanned A, original, MIT.

A pre-interview research workflow gathers public information about a company and a job before an interview, then writes a match analysis. It uses a resume, job description, and optional intake notes.

In plain words
What is it for?
It helps assess how a candidate matches a role and prepare for the conversation. It can research company size, finances, reputation, hiring signals, market context, and location-based salary ranges.
Why use it?
Candidates often have to collect company facts, salary context, reputation signals, and role information from scattered sources. This workflow puts that research into one preparation report.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps assess how a candidate matches a role and prepare for the conversation. It can research company size, finances, reputation, hiring signals, market context, and location-based salary ranges.

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Install with agentmods
npx agentmods add skills/tam159/next-role/hiring-recon
Install

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.

Any agent
npx skills add tam159/next-role --skill hiring-recon
Clone the repo
git clone --depth 1 https://github.com/tam159/next-role

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for hiring-recon

README.md
[![agentmods](https://agentmods.dev/badge/skills/tam159/next-role/hiring-recon/github.svg)](https://agentmods.dev/skills/tam159/next-role/hiring-recon)
Your own site
<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.

agentmods 80×15 button for hiring-recon

Your own site · 80×15
<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>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,109 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 10d ago against content hash 28b85aad17cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

backend/agents/career_agent/skills/hiring-recon/hiring-recon/SKILL.md · 82 lines

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 research
  • web_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

  1. Company snapshot — size, stage (startup / scale-up / public), business model (B2B vs B2C, product vs services/outsourcing), main products, recent news.
  2. Financial & hiring signals — funding rounds, revenue/profitability hints, layoffs, headcount trend, recent leadership changes. Use topic="news" or topic="finance" for these.
  3. Reputation & culture — Glassdoor patterns: report repeated themes in 1-2 star reviews (overwork, attrition, leadership) rather than just the average rating. Tenure signals, controversies.
  4. Hiring team — if discoverable, who the hiring manager / team lead is and their background. Omit this section entirely if no signal.
  5. 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.
  6. Match analysis — given the resume vs. the JD vs. company priorities, strengths to emphasize, gaps to address with adjacent experience. 3-5 bullets each.

Read the full file on GitHub · 82 lines

Changes

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.

  1. 10d ago First seen · 82 lines · 63 tokens per session scan A 28b85aad17cf

Subscribe to this mod's changes

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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