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 hunting-persistence-mechanisms-on-windowsgit 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/hunting-persistence-mechanisms-on-windows)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/hunting-persistence-mechanisms-on-windows"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-persistence-mechanisms-on-windows/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/hunting-persistence-mechanisms-on-windows"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/hunting-persistence-mechanisms-on-windows.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 | $0.00076 | $0.00815 |
| Opus 5 | $0.00038 | $0.00407 |
| Sonnet 5 | $0.00015 | $0.00163 |
| Haiku 4.5 | $0.00008 | $0.00081 |
Grade A, and why
hunting-persistence-mechanisms-on-windows 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 9d 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.
Hunting Persistence Mechanisms on Windows
When to Use
- You are hunting for how an adversary maintains a foothold across reboots.
- You have autostart inventories (Autoruns exports, registry/scheduled-task/WMI dumps) from one or many hosts and want to surface anomalies.
- You want to baseline a fleet and flag entries that deviate or were recently added.
Do not use a single host's autoruns list as ground truth — many legitimate entries look unusual; comparison against a fleet baseline is what makes anomalies meaningful.
Prerequisites
- Autostart data per host: Run/RunOnce keys, services, scheduled tasks, WMI event subscriptions, startup folders, and (ideally) signing status.
- A baseline (golden image or fleet majority) to diff against.
Workflow
Step 1: Inventory the ASEPs
Collect the common Autostart Extensibility Points across hosts: Run keys (T1547.001), services (T1543.003), scheduled tasks (T1053.005), and WMI subscriptions (T1546.003).
Step 2: Baseline and diff
Compare each host's entries to the fleet baseline; entries appearing on few hosts, unsigned, or pointing to user-writable/temp paths bubble up.
python scripts/analyst.py hunt autostart.json --baseline baseline.json
Step 3: Score anomalies
Raise priority for: unsigned binaries, LOLBins launched with encoded args, paths in
%TEMP%/%APPDATA%, recently created tasks, and WMI consumers running scripts.
Step 4: Triage candidates
For each high-score entry, pull the target binary's signing/hash, the creating user/time, and correlate with process and network telemetry.
Step 5: Confirm and respond
Validate suspicious entries (sandbox/RE the binary), escalate confirmed persistence to IR, and turn the pattern into a detection.
Validation
- Anomalies are ranked against a baseline, not flagged from one host in isolation.
- High-score entries map to concrete artifacts (path, hash, signer) for triage.
- Confirmed persistence ties back to a known intrusion timeline.
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.
- 9d ago First seen · 97 lines · 76 tokens per session scan A a2122650d625
hunting-persistence-mechanisms-on-windows is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 815 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 2026-09-03.
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