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.
curl -O https://raw.githubusercontent.com/galiprandi/job-seeker/main/.agents/skills/targets/SKILL.mdgit clone --depth 1 https://github.com/galiprandi/job-seekerWrote 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/galiprandi/job-seeker/targets)<a href="https://agentmods.dev/skills/galiprandi/job-seeker/targets"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/targets/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/galiprandi/job-seeker/targets"><img src="https://agentmods.dev/badge/skills/galiprandi/job-seeker/targets.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 | $0.00027 | $0.04568 |
| Opus 5 | $0.00014 | $0.02284 |
| Sonnet 5 | $0.00005 | $0.00914 |
| Haiku 4.5 | $0.00003 | $0.00457 |
Grade A, and why
targets 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 yesterday.
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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Targets — Active direct sourcing
Trigger
Keyword: targets
The user says targets (or variants: "register on companies", "apply to target companies", "company direct", "direct sourcing") and the full registration + application flow is triggered.
Purpose
The third sourcing pillar alongside radar (passive alerts) and apply (LinkedIn Easy Apply). This flow goes directly to the career sites of the target companies (loaded from users.data.target_companies in DB), registers the user, creates a standout profile, and applies to matching positions.
Pre-flight
- Browser: always use
node scripts/browser.jsfor open/close/goto. See AGENTS.md "Browser session" and "Parallel execution" for details. Never callplaywright-cli opendirectly - Parallel execution: if running alongside other flows (e.g:
applyornews), attach a session withnode scripts/browser.js attach --session targets-1and pass--session targets-1to all browser commands and scripts. Usedetachwhen done (neverclose— it's ref-counted) - Load active preferences (see
memoryskill):node scripts/db.js "SELECT category, key, value, confidence, source FROM preferences WHERE user_id = <user_id> AND status = 'active' ORDER BY category, key" - Load strategy (see AGENTS.md "Strategy levels"):
Respect:node scripts/db.js "SELECT data->'strategy' AS strategy FROM users WHERE id = <user_id>"targets_batch_size(max companies per session, 0 = don't run, "all" = no limit),match_threshold,relax_must_haves. Iftargetsis not insources_active, skip this flow entirely - Load profile, job preferences, CV and photo paths:
node scripts/db.js "SELECT data->'profile' AS profile, data->'job_preferences' AS prefs, data->'cv_path' AS cv_path, data->'photo_path' AS photo_path, data->'style_profile' AS style_profile FROM users WHERE id = <user_id>" - Load company registrations to see current state:
node scripts/db.js "SELECT id, company, region, sector, careers_url, ats_platform, registration_status, profile_completed, applied_jobs_count, notes FROM company_registrations WHERE user_id = <user_id> ORDER BY registration_status, region, company" - Load existing applications for dedup:
node scripts/db.js "SELECT company, url FROM applications WHERE user_id = <user_id>"
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.
- yesterday Changed 90ff951b336a
- 12d ago First seen · 326 lines · 27 tokens per session scan A ab6c7bde3458
targets is a skill published in the GitHub repository galiprandi/job-seeker (26 stars, last pushed 2d ago), licensed MIT. It adds 27 tokens to every session and 4,568 once invoked, about $0.0001 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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