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 skills add swan-gtm/gtm-skills --skill hiring-radargit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/hiring-radar)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/hiring-radar"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/hiring-radar/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/swan-gtm/gtm-skills/hiring-radar"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/hiring-radar.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 | $0.00120 | $0.01252 |
| Opus 5 | $0.00060 | $0.00626 |
| Sonnet 5 | $0.00024 | $0.00250 |
| Haiku 4.5 | $0.00012 | $0.00125 |
Grade A, and why
hiring-radar 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run when target accounts' job postings should work as buying signals. Produces classified signals with dated evidence, a per-signal alert, and a reconciled digest — detection only, never outreach.
Run this daily to keep a live picture of who is building what across your target accounts. Hiring is budget made visible: a team being built today is a tooling decision being made this quarter.
Setup state. Not yet configured for this org. Before scheduling, configure: the account watchlist (tier-A daily, rest weekly), the mention-scan queries (competitor and own products, watched role titles), the signals channel, and your own-domain exclusions. Maintain the configuration of connectors from job-board-sourcing-and-actor-schemas.md here. After setup, rewrite this paragraph to describe the current configuration.
Step 1 — Pull fresh postings (two scans, never conflated)
Watchlist scan: jobs posted BY watched accounts — company-filtered.
Mention scan: jobs at ANY company matching a tracked query — a role title ("data engineer"), a technology in the JD ("SharePoint"), or both ("administrator" + your platform). A JD that mentions a watched company is NOT that company's hiring signal. Pull everything since the last run, paginated to the end. Drop your own postings — a query on your own product returns your own ads. LinkedIn only through native integration, never a third-party scraper; other boards one vetted, test-run scraper each — schemas and the vetting protocol are in references/job-board-sourcing-and-actor-schemas.md.
Step 2 — Keep only GTM-relevant patterns (the threshold)
Tune to your GTM; everything else is log-only:
- Hiring spree — 3+ fresh roles, same team → tooling decisions being made now
- Buyer-persona hire — "first" / "Head of" the persona you sell to → a new owner with a 90-day agenda
- Build-vs-buy tell — JD describes building what you sell → funded, unstaffed, still open
- Executive hire in your buying committee → their first quarter decides what stays
- Competitor tool required in a JD → displacement intel
- Your product required in a JD → a customer signal for the CS owner
What ships with it
1 file 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.
- 9d ago First seen · 106 lines · 120 tokens per session scan A 3156ca062d82
hiring-radar is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 120 tokens to every session and 1,252 once invoked, about $0.0006 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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