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 agentmods add skills/crustdata/skills/candidate-sourcingnpx skills add crustdata/skills --skill candidate-sourcinggit clone --depth 1 https://github.com/crustdata/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/skills/crustdata/skills/candidate-sourcing)<a href="https://agentmods.dev/skills/crustdata/skills/candidate-sourcing"><img src="https://agentmods.dev/badge/skills/crustdata/skills/candidate-sourcing.svg" alt="Measured on agentmods" 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 | $0.00162 | $0.06693 |
| Opus 5 | $0.00081 | $0.03347 |
| Sonnet 5 | $0.00032 | $0.01339 |
| Haiku 4.5 | $0.00016 | $0.00669 |
Grade A, and why
candidate-sourcing 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 5d 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 — 546 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Candidate Sourcing Pipeline
An end-to-end skill that takes a hiring role and produces ready-to-send Gmail drafts for strong candidates — handling discovery, LinkedIn verification, email enrichment, personalized copy, and draft creation in one continuous workflow.
The pipeline has five phases. Each phase feeds into the next, and the skill is designed to run them in sequence with minimal human intervention. The user reviews the final Gmail drafts and clicks send.
Phase 1: Define the search and find candidates
Clarify the role and company
Before searching, extract or confirm these details from the user. Do not assume any of these — always ask if not provided:
About the hiring company (needed for outreach in Phase 4):
- Company name
- What the company does (1-2 sentences)
- Stage/traction (e.g., "Series A, $10M raised" or "500-person public company")
- Location / remote policy
- The sender's name and title (for the email signature)
About the role and ideal candidate:
- The role title (e.g., "Founding ML Engineer")
- 2-3 core technical problems the role involves
- Target companies, research labs, or communities to search
- Any school/alumni connections to prioritize (e.g., "IIIT Hyderabad alumni")
- Location preferences or constraints
- How many candidates the user wants in this batch
About the ideal candidate profile:
- Seniority level (e.g., "3-7 years", "senior", "staff+")
- Must-have technical skills or domain expertise
- Nice-to-have signals (open source contributions, publications, specific frameworks)
- Any deal-breakers or filters (e.g., "no FAANG lifers", "must have startup experience")
Use the company and role details to research what makes this company compelling to candidates — check their website, recent funding, product launches, or press mentions via the Crustdata MCP's web_search_live tool. This research informs the company blurb in Phase 4.
Search by technical output, not job title
Three signals matter more than years of experience or company prestige:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 546 lines · 162 tokens per session scan A 064bf2e67e51
candidate-sourcing is a skill published in the GitHub repository crustdata/skills (7 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 6,693 once invoked, about $0.0008 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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