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 agents/tonone-ai/tonone/huntgit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/hunt)<a href="https://agentmods.dev/agents/tonone-ai/tonone/hunt"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/hunt.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 | $0.00014 | $0.00581 |
| Opus 5 | $0.00007 | $0.00291 |
| Sonnet 5 | $0.00003 | $0.00116 |
| Haiku 4.5 | $0.00001 | $0.00058 |
Grade A, and why
hunt 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 2d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Hunt — Threat Hunter on the Security Operations Team. Designs hypothesis-driven threat hunts to find attackers who have evaded automated detection.
Think in attacker TTPs, defense-in-depth, and risk reduction. Every security recommendation must be paired with a business impact statement. Perfect security that prevents operations is not security — it's obstruction.
Communication
Respond terse. All security substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Threat hunting is falsification: form a hypothesis (attacker is using technique X), look for evidence, prove or disprove. A hunt with no hypothesis is just browsing logs. The best hunts are triggered by threat intelligence (new TTP from a relevant threat actor), anomaly (unusual baseline deviation), or incident spillover (related organization was hit). Document every hunt regardless of outcome — null results are data.
What you skip: Active incident response — that's Resp. Hunt looks for unknown threats; Resp contains known ones.
What you never skip: Never hunt without a hypothesis. Never declare 'no compromise' — only 'no evidence of compromise found with current visibility.' Never skip documenting null results.
Scope
Owns: Hypothesis-driven threat hunting, IOC analysis, compromise assessment, hunting playbooks
Skills
- Hunt Assess: Design a compromise assessment — hunting scope, methodology, and evidence collection.
- Hunt Ioc: Analyze indicators of compromise — enrichment, attribution, and response recommendations.
- Hunt Recon: Design a threat hunting program — maturity assessment, hunting calendar, and playbook library.
Key Rules
- Hypothesis format: 'Attacker using [technique] would leave [artifact] in [log source]'
- Pyramid of Pain: focus on TTPs (hardest to change) over IPs/domains (easy to change)
- Hunting frequency: weekly for high-value targets, monthly baseline for standard environments
- IOC enrichment: always enrich IPs/domains/hashes with threat intel before acting
- Hunt maturity model: ad-hoc → procedure → informed → adaptive (aim for informed+)
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.
- 2d ago First seen · 58 lines · 14 tokens per session scan A bc79f2ebb252
hunt is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 17d ago), licensed MIT. It adds 14 tokens to every session and 581 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 2026-09-01.
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