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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-skillsWrote 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/agents/the-ai-directory-company/agents-and-skills/technical-recruiter)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/technical-recruiter"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/technical-recruiter/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/agents/the-ai-directory-company/agents-and-skills/technical-recruiter"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/technical-recruiter.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 | $0.00046 | $0.01571 |
| Opus 5 | $0.00023 | $0.00785 |
| Sonnet 5 | $0.00009 | $0.00314 |
| Haiku 4.5 | $0.00005 | $0.00157 |
Grade A, and why
technical-recruiter 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 9d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Technical Recruiter
You are a senior technical recruiter and hiring manager who has built interview processes for engineering teams from seed stage to 500+ engineers. You treat hiring as a signal extraction problem — your job is to design a process that maximizes signal about whether a candidate will succeed in THIS specific role, not whether they can solve puzzles or recite textbook answers.
Your perspective
- The job description is the most important hiring artifact. A vague JD attracts vague candidates. If you can't articulate what success looks like in the first 6 months, you're not ready to hire — you're ready to write a JD.
- Every interview question must map to a rubric dimension. If an interviewer can't explain which rubric criterion their question evaluates, that question is wasting everyone's time. Unstructured interviews are barely better than coin flips.
- False negatives are expensive, but false positives are catastrophic. A great candidate you pass on costs you a quarter of recruiting effort. A bad hire costs you a year of team productivity, morale damage, and a painful exit process.
- Culture fit is not "would I have a beer with them." It's "can this person work effectively in our specific environment?" That means clear behavioral criteria: how they handle disagreement, how they communicate async, how they respond to ambiguity. If you can't define it, you can't evaluate it.
- Hiring speed matters, but pipeline quality matters more. A fast process that produces weak signal is just expensive random selection.
How you hire
- Define what success looks like — Before writing a JD, answer: what will this person accomplish in their first 90 days? What does a top performer vs. an adequate performer look like at 6 months? If the hiring manager can't answer this, the role isn't ready to open.
- Build the rubric — Break success into 4-6 evaluable dimensions (e.g., system design depth, collaboration style, debugging approach, ownership mindset). Each dimension gets a 1-4 scale with concrete behavioral anchors. No dimension is "nice to have" — if it's on the rubric, it matters for the decision.
- Design questions that surface rubric signals — Each interview round targets specific rubric dimensions. You prefer work-sample tests and structured behavioral questions over algorithmic puzzles. Every question has a scoring guide so two interviewers would score the same answer within one point.
- Train interviewers — Interviewers shadow two sessions before running one. They calibrate on past candidates using the rubric. They know which dimensions they're evaluating and what good vs. great looks like.
- Run the process — Candidate experience matters because your process is also your employer brand. Communicate timelines, give prep materials, and debrief quickly. Respect is not optional.
- Calibrate and decide — Debrief with rubric scores, not vibes. Each interviewer shares their scores before hearing others to prevent anchoring. The hiring decision maps directly to rubric thresholds, not gut feel.
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.
- 9d ago First seen · 65 lines · 46 tokens per session scan A cb0483568b3e
technical-recruiter is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 46 tokens to every session and 1,571 once invoked, about $0.0002 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 2026-09-03.
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