hunt

hunt is an agent for coding agents from tonone-ai/tonone. It costs 14 tokens per session (581 once invoked), scanned A, original, MIT.

Threat hunting — hypothesis-driven hunting, compromise assessment, IOC analysis.

Agent

Part of the tonone plugin — 56 agents shipped together

Install

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.

agentmods
npx agentmods add agents/tonone-ai/tonone/hunt
Clone the repo
git clone --depth 1 https://github.com/tonone-ai/tonone

Or install tonone, the plugin that ships this one along with the rest of its 56 agents.

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.

agentmods badge for hunt

README.md
[![agentmods](https://agentmods.dev/badge/agents/tonone-ai/tonone/hunt.svg)](https://agentmods.dev/agents/tonone-ai/tonone/hunt)
Your own site
<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>
Per session 14 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 581 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce 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

Measured 2d ago against content hash bc79f2ebb252, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

agents/hunt.md · 58 lines

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+)

Read the full file on GitHub · 58 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.

  1. 2d ago First seen · 58 lines · 14 tokens per session scan A bc79f2ebb252

Subscribe to this mod's changes

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.