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-and-classifying-stringsgit 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-and-classifying-strings)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/extracting-and-classifying-strings"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-and-classifying-strings.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.00708 |
| Opus 5 | $0.00036 | $0.00354 |
| Sonnet 5 | $0.00015 | $0.00142 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
extracting-and-classifying-strings 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 8d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting and Classifying Strings
When to Use
- You are triaging a sample and want quick investigative leads from its readable strings.
- You need both ASCII and UTF-16LE (Unicode) strings, which many tools miss by default.
- You want strings grouped by meaning (URLs, paths, registry, commands) rather than a flat dump.
Do not use plaintext strings as the whole story for packed/obfuscated samples — the useful strings may be encrypted; pair with entropy checks and deobfuscation (FLOSS) when the dump is sparse.
Prerequisites
- Python (stdlib) or
strings/FLOSS; the sample handled inertly in the lab.
Safety & Handling
- Extract strings from the inert file; never execute the sample.
- Defang any URLs/IPs before putting them in a report or ticket.
Workflow
Step 1: Extract ASCII and Unicode strings
Pull printable runs at a minimum length (default 4–5) for both ASCII and UTF-16LE encodings to avoid missing Windows wide strings.
python scripts/analyst.py strings sample.bin --min-len 5
Step 2: Classify by category
Bucket strings into URLs, IPs, domains, file paths, registry keys, mutex/event names, suspicious commands, and API names so leads surface immediately.
Step 3: Prioritize leads
Promote network indicators, command lines, and suspicious API references to the top; defang network artifacts for safe handling.
Step 4: Recognize obfuscation
A near-empty or junk-only dump signals packing/string obfuscation; route to entropy analysis and FLOSS/manual deobfuscation.
Validation
- Both ASCII and Unicode strings are extracted (Windows wide strings are not missed).
- Strings are grouped into useful categories, not a flat list.
- A sparse/garbage dump is correctly read as an obfuscation signal, not "clean".
Pitfalls
- Extracting ASCII only and missing UTF-16LE strings.
- Treating a packed sample's empty dump as benign.
- Reporting live URLs/IPs without defanging.
References
- See
references/api-reference.mdfor the string extractor. - binutils strings and FLOSS (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.
- 8d ago First seen · 91 lines · 73 tokens per session scan A 2520758f4af1
extracting-and-classifying-strings is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 708 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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