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-encryption-keys-from-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/extracting-encryption-keys-from-binaries)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/extracting-encryption-keys-from-binaries"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-encryption-keys-from-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/extracting-encryption-keys-from-binaries"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/extracting-encryption-keys-from-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 | $0.00076 | $0.00707 |
| Opus 5 | $0.00038 | $0.00353 |
| Sonnet 5 | $0.00015 | $0.00141 |
| Haiku 4.5 | $0.00008 | $0.00071 |
Grade A, and why
extracting-encryption-keys-from-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 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 Encryption Keys From Binaries
When to Use
- You have a sample with hardcoded crypto and want to surface candidate keys/IVs (16/24/32-byte high-entropy blocks) and passphrase strings.
- You are recovering material to decrypt config, C2 traffic, or ransomware test vectors.
Do not use entropy alone as proof a region is a key — corroborate with proximity to crypto constants/APIs and by attempting decryption. This skill reads the binary statically and executes nothing.
Prerequisites
- The binary (read inertly). Pairs well with the crypto-constant scanner.
Safety & Handling
- Read bytes statically; handle recovered keys as sensitive and store securely.
Workflow
Step 1: Find high-entropy key-sized regions
python scripts/analyst.py keys sample.bin
Slides a window of 16/24/32 bytes and reports regions whose entropy exceeds a threshold (likely random key material), with offsets.
Step 2: Surface printable secrets
Extract printable strings that look like passphrases/hex keys (e.g., 32/64-hex, base64 of 16/32 bytes).
Step 3: Corroborate
Rank candidates by proximity to crypto constants/API references and confirm by decrypting a known ciphertext.
Step 4: Document
Record candidate keys, sizes, offsets, and the corroborating evidence.
Validation
- Candidate regions are exactly key-sized (16/24/32 bytes) and high-entropy.
- Hex/base64 key strings are length-consistent with a real key size.
- A candidate is confirmed only by successful decryption, not entropy alone.
Pitfalls
- Compressed/packed data producing many false high-entropy regions — unpack first.
- Keys derived at runtime (KDF) not present statically.
- Endianness/encoding of the key (raw vs hex vs base64) before use.
References
- See
references/api-reference.mdfor the extractor. - FIPS 197 and ATT&CK T1573 (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 · 91 lines · 76 tokens per session scan A 95e911cdabf1
extracting-encryption-keys-from-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 76 tokens to every session and 707 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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