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 meltedinhex/analyst-ai-pack --skill building-a-threat-hunt-hypothesisgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/building-a-threat-hunt-hypothesis)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/building-a-threat-hunt-hypothesis"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-a-threat-hunt-hypothesis/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/meltedinhex/analyst-ai-pack/building-a-threat-hunt-hypothesis"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/building-a-threat-hunt-hypothesis.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 | $0.00067 | $0.00818 |
| Opus 5 | $0.00034 | $0.00409 |
| Sonnet 5 | $0.00013 | $0.00164 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
building-a-threat-hunt-hypothesis 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 11d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a Threat-Hunt Hypothesis
When to Use
- You are starting a proactive, hypothesis-driven hunt rather than responding to an alert.
- You need to turn a vague concern ("are we seeing living-off-the-land abuse?") into a testable, scoped statement with clear data requirements.
- You want a repeatable plan that produces either a detection, a gap, or a confident negative.
Do not use this as an incident-response trigger — if you already have a confirmed alert, pivot to investigation/IR, not hypothesis generation.
Prerequisites
- Knowledge of your environment's telemetry (EDR, Sysmon, proxy, DNS, auth logs) and retention.
- Familiarity with ATT&CK to anchor the behavior you intend to hunt.
Workflow
Step 1: Pick a behavior, not a tool
Anchor on an adversary behavior (an ATT&CK technique/sub-technique) you have reason to expect given your threat model, not a specific product alert.
Step 2: Write a testable hypothesis
Use the form: "If [actor behavior], then I expect to observe [evidence] in [data source]."
If an adversary uses WMI for lateral movement (T1047), then I expect to observe
wmiprvse.exe spawning command interpreters on hosts that do not normally do so,
in Sysmon process-creation (Event ID 1).
Step 3: Map to data and logic
Confirm the data source exists and is retained, then define the concrete query/detection logic and what "normal" looks like (baseline) so anomalies stand out.
python scripts/analyst.py plan --technique T1047 --datasource "Sysmon EID1" --window 14d
Step 4: Set scope and success criteria
Define the host/time scope, the threshold for "interesting," and the three possible outcomes: finding (→ IR), detection gap (→ engineering), or confident negative (documented).
Step 5: Record and hand off
Capture the hypothesis, queries, and outcome so it becomes a repeatable, version-controlled hunt — successful logic becomes a detection rule.
Validation
- The hypothesis is falsifiable and names a specific data source that actually exists.
- A baseline of "normal" is defined so results are interpretable.
- Each outcome has a defined next action (IR, detection engineering, or documented negative).
What ships with it
3 files 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.
- 11d ago First seen · 97 lines · 67 tokens per session scan A 51925ee94803
building-a-threat-hunt-hypothesis is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 67 tokens to every session and 818 once invoked, about $0.0003 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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