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 identifying-cryptographic-routines-in-binariesgit 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/identifying-cryptographic-routines-in-binaries)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/identifying-cryptographic-routines-in-binaries"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/identifying-cryptographic-routines-in-binaries/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/identifying-cryptographic-routines-in-binaries"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/identifying-cryptographic-routines-in-binaries.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 | $0.00080 | $0.00696 |
| Opus 5 | $0.00040 | $0.00348 |
| Sonnet 5 | $0.00016 | $0.00139 |
| Haiku 4.5 | $0.00008 | $0.00070 |
Grade A, and why
identifying-cryptographic-routines-in-binaries 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.
What it actually says
Identifying Cryptographic Routines in Binaries
When to Use
- You need to determine which cryptographic algorithms a sample implements (ransomware crypto, C2 encryption, config protection) by locating constant tables.
- You want fast triage before manually reversing the crypto routine.
Do not use constant detection as proof of a specific mode/usage — it identifies the primitive, not how it is applied. This skill reads the binary statically and executes nothing.
Prerequisites
- The binary (read inertly).
Safety & Handling
- Read bytes statically; treat the sample as malicious data.
Workflow
Step 1: Scan for crypto constants
python scripts/analyst.py scan sample.bin
Searches for AES S-box/Te tables, SHA-256 H/K init constants, MD5 init, ChaCha/Salsa expand 32-byte k sigma, the CRC32 polynomial table, and the base64 alphabet, reporting offsets.
Step 2: Corroborate with imports/strings
Pair constant hits with crypto API imports (CryptDecrypt, BCryptEncrypt, EVP_*) or library
strings to confirm.
Step 3: Locate the routine
Use the constant offset to find the referencing function for deeper reversing/key extraction.
Step 4: Document
Record which primitives are present and where, mapping to behavior (e.g., AES → file encryption).
Validation
- Each hit references a real, named constant table (not a coincidental byte run).
- Detected primitives are corroborated by imports/strings where possible.
- Offsets point into the binary and can be navigated in a disassembler.
Pitfalls
- Statically linked crypto libraries adding constants unused by the malware's logic.
- Custom/modified S-boxes evading exact-match detection.
- Assuming AES presence implies ransomware — corroborate with behavior.
References
- See
references/api-reference.mdfor the scanner. - FIPS 197 and FIPS 180-4 constant references (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.
- 9d ago First seen · 89 lines · 80 tokens per session scan A 274815871296
identifying-cryptographic-routines-in-binaries is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 696 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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