A job-search workflow for finding useful contacts at target companies, such as personal connections, university alumni, former colleagues, and recruiters. It can prepare referral requests or direct messages and adjust a CV to a job description.

Find first- and second-degree contacts, alumni, former coworkers, and recruiters; prepare outreach in the database; and tailor a CV to a target role's requirements.

A referral or warm introduction can provide a route into a company beyond applying cold, while matching the CV to the job can make the application more relevant. The workflow also follows the user's outreach settings.

How galiprandi/job-seeker configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to galiprandi/job-seeker. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/galiprandi/job-seeker/main/.agents/skills/referrals/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/galiprandi/job-seeker

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/galiprandi/job-seeker/referrals/github.svg)](https://agentmods.dev/skills/galiprandi/job-seeker/referrals)

Your own site

<a href="https://agentmods.dev/skills/galiprandi/job-seeker/referrals"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/referrals/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/galiprandi/job-seeker/referrals"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/referrals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,411 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.

Model	Per session	Once invoked
Fable 5.1	$0.00055	$0.01411
Opus 5	$0.00028	$0.00705
Sonnet 5	$0.00011	$0.00282
Haiku 4.5	$0.00006	$0.00141

Measured today against content hash 0607e57d15ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

referrals 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 today.

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.

.agents/skills/referrals/SKILL.md · 119 lines

How it starts

The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Warm Sourcing & Referrals

Trigger

Keyword: referrals (or variants: "warm sourcing", "buscar contactos", "solicitar referido")

The user says referrals or launches warm sourcing for a target company/role. Also executed as step 0 of the apply and targets flows to maximize conversion.

Flow

0. Pre-flight

Verify active browser session (see AGENTS.md "Browser session"): node scripts/browser.js open <url> --headed (Gold Rule 5) if session closed

Load profile, university background, past companies, and job preferences from Postgres DB:

node scripts/db.js "SELECT data->'profile' AS profile, data->'job_preferences' AS prefs, data->'style_profile' AS style FROM users WHERE id = <user_id>"

Load strategy (see AGENTS.md "Strategy levels"):
```
node scripts/db.js "SELECT data->'strategy' AS strategy FROM users WHERE id = <user_id>"
```
Respect: cold_outreach (gates the recruiter-outreach branch in step 3). If referrals is not in sources_active, the flow should not run standalone — when invoked as step 0 of apply/targets, those flows handle the gate.

Load active preferences (see memory skill):

node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = <user_id> AND status = 'active' ORDER BY category, key"

1. Warm Contact & Recruiter Discovery

For a target company and role:

# Automated discovery script
node scripts/linkedin-warm-sourcing.js --company "<Company>" --role "<Role>" --json

The script searches for:

1st & 2nd degree connections currently working at <Company>
University alumni (matching institutions from users.data.profile.education)
Ex-colleagues (matching past employers from users.data.profile.experience)
Recruiters & Hiring Managers assigned to the role/company

2. Referral Request Staging (Highest Conversion — Strategy #1)

If an internal contact, alumni, or ex-colleague is found:

Read the full file on GitHub · 119 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.

today Changed 0607e57d15ad
11d ago First seen · 119 lines · 55 tokens per session scan A ab63cc114c16

Subscribe to this mod's changes

referrals is a skill published in the GitHub repository galiprandi/job-seeker (26 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 1,411 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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