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 extracting-config-from-a-running-samplegit 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/extracting-config-from-a-running-sample)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/extracting-config-from-a-running-sample"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-config-from-a-running-sample/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/extracting-config-from-a-running-sample"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-config-from-a-running-sample.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 | $0.00075 | $0.00699 |
| Opus 5 | $0.00037 | $0.00349 |
| Sonnet 5 | $0.00015 | $0.00140 |
| Haiku 4.5 | $0.00007 | $0.00070 |
Grade A, and why
extracting-config-from-a-running-sample 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 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.
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.
What it actually says
Extracting Config From a Running Sample
When to Use
- You have a process memory dump of a detonated sample whose config is decrypted in memory.
- You need to recover C2 endpoints, ports, campaign/botnet IDs, mutexes, and keys.
Do not use this to detonate the sample — it consumes a dump already captured in an isolated sandbox. For static-only config decryption, use the dedicated config-decryptor workflow.
Prerequisites
- A process memory dump (
.dmp/raw region) captured after the config was decrypted.
Safety & Handling
- Treat the dump as malicious data; read it inertly and defang recovered endpoints.
Workflow
Step 1: Hunt config indicators
python scripts/analyst.py hunt process.dmp
Scans for URLs, IPv4:port pairs, mutex-like tokens, base64 blobs, and printable key/value candidates in decrypted regions.
Step 2: Decode obfuscation layers
For candidate blobs, try common transforms (single-byte XOR brute force, base64) and re-scan the decoded output for endpoints.
Step 3: Structure and validate the config
Assemble recovered fields into a structured config and sanity-check (valid hosts, plausible ports, consistent campaign IDs).
Step 4: Defang and report
Defang hosts/URLs and document the offsets where the config was recovered.
Validation
- Recovered endpoints are real, parseable hosts/ports — not random byte noise.
- XOR/base64 decode is confirmed by the decoded output containing new endpoints/strings.
- The final config is defanged before sharing.
Pitfalls
- Mistaking unrelated in-memory URLs (browser, OS) for C2 — corroborate with the sample's region.
- Single-byte XOR brute force producing coincidental strings; require multiple corroborating hits.
- Reporting raw, live C2 endpoints without defanging.
References
- See
references/api-reference.mdfor the hunter/decoder. - ATT&CK T1140 and T1027 (linked in frontmatter).
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.
- 10d ago First seen · 89 lines · 75 tokens per session scan A 4770dc57bf43
extracting-config-from-a-running-sample is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 699 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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