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 onfire7777/universal-ai-skills-library --skill analyzing-ransomware-encryption-mechanismsgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/analyzing-ransomware-encryption-mechanisms)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/analyzing-ransomware-encryption-mechanisms"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-ransomware-encryption-mechanisms/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/onfire7777/universal-ai-skills-library/analyzing-ransomware-encryption-mechanisms"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-ransomware-encryption-mechanisms.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 | $0.00079 | $0.03145 |
| Opus 5 | $0.00039 | $0.01572 |
| Sonnet 5 | $0.00016 | $0.00629 |
| Haiku 4.5 | $0.00008 | $0.00314 |
Grade A, and why
analyzing-ransomware-encryption-mechanisms 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.
This is a copy
95% identical to analyzing-ransomware-encryption-mechanisms — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Ransomware Encryption Mechanisms
When to Use
- A ransomware infection has occurred and recovery requires understanding the encryption scheme used
- Assessing whether decryption is possible without paying the ransom (implementation flaws, known decryptors)
- Reverse engineering ransomware to identify the encryption algorithm, key derivation, and key storage mechanism
- Developing a decryptor tool when a weakness in the ransomware's cryptographic implementation is identified
- Classifying a ransomware sample by its encryption approach to attribute it to a known family
Do not use for production data recovery operations without first verifying the decryption method on test copies of encrypted files.
Prerequisites
- Ghidra or IDA Pro for reverse engineering the ransomware binary
- Python 3.8+ with
pycryptodomelibrary for testing encryption/decryption routines - Sample encrypted files and their corresponding plaintext originals (known-plaintext pairs)
- Access to the ransomware binary (unpacked if applicable)
- Familiarity with symmetric (AES, ChaCha20) and asymmetric (RSA) cryptographic algorithms
- NoMoreRansom.org database for checking existing free decryptors
Workflow
Step 1: Identify the Encryption Algorithm
Determine which cryptographic algorithm the ransomware uses:
# Check for Windows Crypto API usage in imports
import pefile
pe = pefile.PE("ransomware.exe")
crypto_apis = {
"CryptAcquireContextA": "Windows CryptoAPI",
"CryptAcquireContextW": "Windows CryptoAPI",
"CryptGenKey": "Windows CryptoAPI key generation",
"CryptEncrypt": "Windows CryptoAPI encryption",
"CryptImportKey": "Windows CryptoAPI key import",
"BCryptOpenAlgorithmProvider": "Windows CNG (modern crypto)",
"BCryptEncrypt": "Windows CNG encryption",
"BCryptGenerateKeyPair": "Windows CNG asymmetric key gen",
}
print("Crypto API Imports:")
for entry in pe.DIRECTORY_ENTRY_IMPORT:
for imp in entry.imports:
if imp.name and imp.name.decode() in crypto_apis:
print(f" {entry.dll.decode()} -> {imp.name.decode()}: {crypto_apis[imp.name.decode()]}")
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 · 328 lines · 79 tokens per session scan A c1d1f4c58389
analyzing-ransomware-encryption-mechanisms is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 3,145 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to analyzing-ransomware-encryption-mechanisms, differing in 40 lines, and is treated as a copy.
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