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 analyzing-infostealer-credential-theftgit 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/analyzing-infostealer-credential-theft)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-infostealer-credential-theft"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-infostealer-credential-theft/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/analyzing-infostealer-credential-theft"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-infostealer-credential-theft.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 | $0.00074 | $0.00681 |
| Opus 5 | $0.00037 | $0.00341 |
| Sonnet 5 | $0.00015 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
analyzing-infostealer-credential-theft 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.
What it actually says
Analyzing Infostealer Credential Theft
When to Use
- You have an infostealer sample and need to enumerate what it targets: browser credential/cookie stores, crypto wallets, FTP/VPN/messaging app configs, and the exfil channel.
- You are building detections from the file paths and endpoints a stealer references.
Do not use this to recover credentials yourself — it characterizes what the malware targets from inert static analysis.
Prerequisites
- The sample (read inertly), optionally with extracted strings.
Safety & Handling
- Read bytes statically; defang exfil endpoints; never run the stealer.
Workflow
Step 1: Map targeted artifacts
python scripts/analyst.py profile sample.bin
Matches references to known browser paths (Login Data, Cookies, Web Data), wallet
directories, app config paths, and credential APIs, grouped by category.
Step 2: Identify exfiltration channel
Detects HTTP(S) POST endpoints, Telegram bot tokens, Discord webhooks, and FTP/SMTP usage in strings.
Step 3: Build the target/exfil profile
Summarize targeted stores and the exfil channel, mapping to ATT&CK.
Step 4: Defang and report
Defang endpoints and produce IOCs for detection.
Validation
- Targeted artifacts are grouped (browsers, wallets, apps, system credential stores).
- The exfil channel is identified with a defanged endpoint where present.
- Findings map to ATT&CK credential-access/collection/exfiltration techniques.
Pitfalls
- Generic browser paths can appear in benign tools — corroborate with theft behavior.
- Missing wallet/app targets that use obfuscated path strings.
- Reporting live exfil endpoints (webhooks, bot tokens) without defanging.
References
- See
references/api-reference.mdfor the profiler. - ATT&CK T1555.003 and T1539 (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.
- 12d ago First seen · 89 lines · 74 tokens per session scan A d6e829aabec5
analyzing-infostealer-credential-theft is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 681 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.
Other skills, from other repositories
analyzing-golang-malware-with-ghidra
Reverse engineer Go-compiled malware in Ghidra by parsing Go buildinfo and pclntab structures, recovering stripped/obfuscated function names (e.g. via GoResolver), and extracting embedded module/dependency strings and types from Go binaries. Use when analyzing a Go-language malware sample, deobfuscating a…
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects. Use when triaging a suspicious PDF attachment from a phishing email, analyzing a PDF-based exploit document, or building detection signatures for weaponized PDF…
analyzing-malicious-pdf-with-peepdf
A Chinese-language skill for examining suspicious PDF files with peepdf, pdfid, and pdf-parser. It is intended for static malware analysis, which studies a file without running it.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.
analyzing-malicious-pdf-with-peepdf
Perform static analysis of malicious PDF documents using peepdf, pdfid, and pdf-parser to extract embedded JavaScript, shellcode, and suspicious objects.