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 EvilFreelancer/secs --skill hunting-threatsgit clone --depth 1 https://github.com/EvilFreelancer/secsWrote 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/evilfreelancer/secs/hunting-threats)<a href="https://agentmods.dev/skills/evilfreelancer/secs/hunting-threats"><img src="https://agentmods.dev/badge/skills/evilfreelancer/secs/hunting-threats/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/evilfreelancer/secs/hunting-threats"><img src="https://agentmods.dev/badge/skills/evilfreelancer/secs/hunting-threats.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00078 | $0.03384 |
| Opus 5 | $0.00039 | $0.01692 |
| Sonnet 5 | $0.00016 | $0.00677 |
| Haiku 4.5 | $0.00008 | $0.00338 |
Grade A, and why
hunting-threats scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Execution via LOLBins | `rundll32`, `regsvr32`, `mshta`, `certutil`, `bitsadmin`, `msiexec` with network or unusual arguments; `curl`/`wget` piping to a shell on Linux | This is a copy
100% identical to hunting-threats — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting Threats
Hunting starts from an assumption of failure: the controls are deployed, no alert has fired, and the adversary may still be present. The output is not usually a compromise — it is a detection, a telemetry gap, or a documented negative result. Hunts that only count as successful when they find something degrade into confirmation bias.
When to Use
- Proactively searching for compromise that detection missed
- Testing a specific hypothesis about attacker behaviour in your environment
- Operationalizing a threat intel report against your telemetry
- Validating that a control or detection actually works in production
- Baselining an environment to enable future outlier analysis
When NOT to Use
- Working an alert queue rather than a hypothesis — use
triaging-security-alerts; a hunt starts from a question, triage from a queue - Confirmed incident in progress — use
responding-to-incidents - Writing the rule for what you found — use
engineering-detections - Sample analysis — use
analyzing-malware - A packet capture to work through — use
analyzing-network-traffic - A confirmed AWS compromise to investigate — use
investigating-aws-incidents - Pivoting on indicators, tracking an actor, or producing a finished intel
product — use
producing-threat-intelligence; a hunt consumes intelligence, it does not produce it - Offensive testing of defenses — use the red team skills
Hypothesis Before Query
An unstructured search through logs is browsing, not hunting. Every hunt gets a written hypothesis in this shape:
Hypothesis: An adversary with [access level] is using [technique] to [objective], which would produce [observable] in [data source], which is distinguishable from normal because [discriminator].
If true, I expect to see: ... If false, I expect: ... Telemetry required: ... (verified present: yes/no)
If you cannot name the discriminator — what makes the malicious instance look different from the thousands of benign ones — the hunt is not ready. Go find the discriminator first; that research is the hunt.
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.
- 10d ago First seen · 286 lines · 78 tokens per session scan A 13c9a1234790
hunting-threats is a skill published in the GitHub repository EvilFreelancer/secs (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 3,384 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to hunting-threats, differing in 0 lines, and is treated as a copy.
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