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 miru-zero/zero-brain --skill classical-cipher-analysisgit clone --depth 1 https://github.com/miru-zero/zero-brainWrote 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/miru-zero/zero-brain/classical-cipher-analysis)<a href="https://agentmods.dev/skills/miru-zero/zero-brain/classical-cipher-analysis"><img src="https://agentmods.dev/badge/skills/miru-zero/zero-brain/classical-cipher-analysis/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/miru-zero/zero-brain/classical-cipher-analysis"><img src="https://agentmods.dev/badge/skills/miru-zero/zero-brain/classical-cipher-analysis.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 | $0.00053 | $0.05824 |
| Opus 5.5 | $0.00021 | $0.02330 |
| Sonnet 5.5 | $0.00011 | $0.01165 |
| Haiku 4.5 | $0.00005 | $0.00582 |
Grade A, and why
classical-cipher-analysis 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 1mo 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 classical-cipher-analysis — 7 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 — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SKILL: Classical Cipher Analysis — Expert Cryptanalysis Playbook
AI LOAD INSTRUCTION: Expert classical cipher identification and breaking techniques for CTF. Covers cipher identification methodology (frequency analysis, IC, Kasiski), monoalphabetic substitution, Caesar/ROT, Vigenere, Enigma, affine, Hill, transposition ciphers, Bacon/Polybius/Playfair, and XOR ciphers. Base models often skip the identification step and jump to the wrong cipher type, or fail to recognize encoded (base64/hex) ciphertext that needs decoding before analysis.
0. RELATED ROUTING
- symmetric-cipher-attacks when dealing with modern symmetric ciphers (AES/DES) rather than classical
- hash-attack-techniques when the challenge involves hash-based constructions
- lattice-crypto-attacks when knapsack-based ciphers are encountered
Quick identification guide
| Observation | Likely Cipher | First Action |
|---|---|---|
| All uppercase letters, uneven frequency | Monoalphabetic substitution | Frequency analysis |
| All uppercase, flat frequency distribution | Polyalphabetic (Vigenere) | IC + Kasiski |
| Only A-Z shifted uniformly | Caesar/ROT | Brute force 25 shifts |
| Base64 alphabet (A-Za-z0-9+/=) | Base64 encoded (decode first) | Base64 decode |
| Hex string (0-9a-f) | Hex encoded (decode first) | Hex decode |
| Binary (0s and 1s) | Binary encoded | Convert to ASCII |
| Dots and dashes | Morse code | Morse decode |
| Raised/normal text pattern | Bacon cipher | Map to A/B, decode |
| 2-digit number pairs (11-55) | Polybius square | Grid lookup |
| Text appears scrambled (right letters, wrong order) | Transposition | Anagram analysis |
| Non-printable bytes XOR-like | XOR cipher | Single/repeating key XOR analysis |
1. CIPHER IDENTIFICATION METHODOLOGY
1.1 Step 1: Character Set Analysis
def analyze_charset(ciphertext):
"""Identify encoding/cipher by character set."""
chars = set(ciphertext.strip())
if chars <= set('01 \n'):
return "Binary encoding"
if chars <= set('.-/ \n'):
return "Morse code"
if chars <= set('0123456789abcdef \n'):
return "Hex encoding"
if chars <= set('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\n'):
if '=' in ciphertext or len(ciphertext) % 4 == 0:
return "Base64 encoding"
if chars <= set('ABCDEFGHIJKLMNOPQRSTUVWXYZ \n'):
return "Uppercase only — classical cipher"
if all(c in '12345' for c in ciphertext.replace(' ', '').replace('\n', '')):
return "Polybius square (digits 1-5)"
return "Mixed charset — needs further analysis"
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.
- 1mo ago First seen · 661 lines · 53 tokens per session scan A d7ad32c44e70
classical-cipher-analysis is a skill published in the GitHub repository miru-zero/zero-brain (0 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 5,824 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to classical-cipher-analysis, differing in 7 lines, and is treated as a copy.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…