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 collecting-volatile-evidence-from-a-suspect-hostgit 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/collecting-volatile-evidence-from-a-suspect-host)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/collecting-volatile-evidence-from-a-suspect-host"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/collecting-volatile-evidence-from-a-suspect-host/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/collecting-volatile-evidence-from-a-suspect-host"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/collecting-volatile-evidence-from-a-suspect-host.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 | $0.00071 | $0.00774 |
| Opus 5 | $0.00036 | $0.00387 |
| Sonnet 5 | $0.00014 | $0.00155 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
collecting-volatile-evidence-from-a-suspect-host 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 12d 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.
Collecting Volatile Evidence from a Suspect Host
When to Use
- A host is suspected of compromise and you must capture state that disappears on reboot.
- You are performing live response before isolating or imaging the machine.
- You need an ordered, integrity-preserving collection for later analysis.
Do not use this as a substitute for full disk imaging when persistence matters — volatile collection complements, not replaces, a forensic image. And do not reboot before collecting.
Prerequisites
- Trusted, statically linked collection tools run from external media (not host binaries).
- Authorization to collect, and a destination on external/write-once media.
Safety & Handling
- Assume host tools are compromised; use your own trusted binaries to avoid rootkit deception.
- Hash every artifact at collection time and record actions for chain of custody; minimize changes to the host.
Workflow
Step 1: Follow the order of volatility
Collect most-volatile first (RFC 3227): CPU/registers and cache → RAM → network state and connections → running processes → logged-on users/sessions → open files/handles → then disk.
Step 2: Capture memory first
Acquire a full RAM image with a trusted acquisition tool before anything that alters memory; it is the richest and most perishable source.
Step 3: Snapshot network and process state
Record active connections, listening ports, ARP/DNS cache, running processes with command lines and parent links, and loaded modules.
python scripts/analyst.py manifest ./collection --case IR-42 --host WS01
Step 4: Record users and handles
Capture logged-on users, sessions, scheduled tasks, and open handles/files.
Step 5: Hash, log, and hand off
Hash each artifact, write a collection manifest with timestamps, and transfer to the analysis environment preserving integrity.
Validation
- Artifacts are collected in order of volatility, memory first, before any reboot.
- Every artifact has a recorded hash and collection timestamp in the manifest.
- Trusted external tools were used; host changes are documented.
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.
- 12d ago First seen · 97 lines · 71 tokens per session scan A a1dcc2cd0d8e
collecting-volatile-evidence-from-a-suspect-host is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 71 tokens to every session and 774 once invoked, about $0.0004 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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