Claude Octopus is an orchestration project that sends research, design, and coding tasks to Claude Code and other AI model providers so their results can be compared. Developers use it for multi-model work, disagreement detection, reviews, persistent context, and an optional workflow that moves from discovery through delivery. The catalogue entries are its commands, skills, agents, instructions, hooks, plugins, and settings.
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 nyldn/claude-octopus --skill skill-extractgit clone --depth 1 https://github.com/nyldn/claude-octopusWrote 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/nyldn/claude-octopus/skill-extract)<a href="https://agentmods.dev/skills/nyldn/claude-octopus/skill-extract"><img src="https://agentmods.dev/badge/skills/nyldn/claude-octopus/skill-extract.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 | $0.00019 | $0.01968 |
| Opus 5 | $0.00010 | $0.00984 |
| Sonnet 5 | $0.00004 | $0.00394 |
| Haiku 4.5 | $0.00002 | $0.00197 |
Grade A, and why
skill-extract 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 2d 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
92% identical to skill-extract — 9 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than
/octo:*slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, seeskills/blocks/codex-host-adapter.md.
Extract Skill - Implementation Guide
Overview
The extract skill provides comprehensive reverse-engineering capabilities for design systems and product architectures. It transforms undocumented codebases into structured, implementation-ready documentation.
Capabilities
Design System Extraction
- Token Extraction: Colors, typography, spacing, shadows from code or CSS
- Component Analysis: Props, variants, usage patterns across React/Vue/Svelte
- Pattern Detection: Layout patterns, design rules, accessibility guidelines
- Storybook Generation: Auto-generated stories with variants and controls
Product Architecture Extraction
- Service Detection: Microservice boundaries, modules, domain boundaries
- API Mapping: REST, GraphQL, tRPC, gRPC endpoint cataloging
- Data Modeling: ORM schema extraction (Prisma, TypeORM, Sequelize)
- Feature Inventory: Route-based and domain-based feature detection
- C4 Diagrams: Automated architecture visualization (Mermaid)
Technical Implementation
Token Extraction Pipeline
Priority Order (High to Low Confidence):
- Code-Defined (95%):
theme.ts,tokens.json, Tailwind config - CSS Variables (90%):
:rootdeclarations - Computed Styles (60%): DOM analysis
- Inferred (40-60%): Color clustering, scale detection
Color Clustering Algorithm:
- Uses CIEDE2000 for perceptually-accurate color distance
- K-means++ initialization for stable clustering
- Default k=8 clusters for primary palettes
- ΔE < 2 threshold for duplicate detection
Component Analysis
What ships with it
1 file 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.
- 2d ago First seen · 238 lines · 19 tokens per session scan A ddcf0ce3a1de
skill-extract is a skill published in the GitHub repository nyldn/claude-octopus (4,054 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 1,968 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to skill-extract, differing in 9 lines, and is treated as a copy.
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