Claude Code Guide is a reference collection for configuring and using Claude Code, Anthropic’s command-line coding agent. Developers use it to learn commands, skills, agents, MCP, automation, security, integrations, and troubleshooting. Its catalogue add-ons provide many of the documented skills and agents.
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 zebbern/claude-code-guide --skill deep-module-refactorgit clone --depth 1 https://github.com/zebbern/claude-code-guideWrote 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/zebbern/claude-code-guide/deep-module-refactor)<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/deep-module-refactor"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/deep-module-refactor/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/zebbern/claude-code-guide/deep-module-refactor"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/deep-module-refactor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 76 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00062 | $0.00866 |
| Opus 5.5 | $0.00025 | $0.00346 |
| Sonnet 5.5 | $0.00012 | $0.00173 |
| Haiku 4.5 | $0.00006 | $0.00087 |
Grade A, and why
deep-module-refactor 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- deep-module-refactor — 100% identical, 0 lines differ
- improve-codebase-architecture — 97% identical, 3 lines differ
- improve-codebase-architecture — 97% identical, 89 lines differ
- improve-codebase-architecture — 97% identical, 5 lines differ
- improve-codebase-architecture — 97% identical, 2 lines differ
- improve-codebase-architecture — 97% identical, 2 lines differ
- improve-codebase-architecture — 97% identical, 2 lines differ
- improve-codebase-architecture — 95% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Codebase Architecture
Explore a codebase like an AI would, surface architectural friction, discover opportunities for improving testability, and propose module-deepening refactors as GitHub issue RFCs.
A deep module (John Ousterhout, "A Philosophy of Software Design") has a small interface hiding a large implementation. Deep modules are more testable, more AI-navigable, and let you test at the boundary instead of inside.
Process
1. Explore the codebase
Use the Agent tool with subagent_type=Explore to navigate the codebase naturally. Do NOT follow rigid heuristics — explore organically and note where you experience friction:
- Where does understanding one concept require bouncing between many small files?
- Where are modules so shallow that the interface is nearly as complex as the implementation?
- Where have pure functions been extracted just for testability, but the real bugs hide in how they're called?
- Where do tightly-coupled modules create integration risk in the seams between them?
- Which parts of the codebase are untested, or hard to test?
The friction you encounter IS the signal.
2. Present candidates
Present a numbered list of deepening opportunities. For each candidate, show:
- Cluster: Which modules/concepts are involved
- Why they're coupled: Shared types, call patterns, co-ownership of a concept
- Dependency category: See REFERENCE.md for the four categories
- Test impact: What existing tests would be replaced by boundary tests
Do NOT propose interfaces yet. Ask the user: "Which of these would you like to explore?"
3. User picks a candidate
4. Frame the problem space
Before spawning sub-agents, write a user-facing explanation of the problem space for the chosen candidate:
- The constraints any new interface would need to satisfy
- The dependencies it would need to rely on
- A rough illustrative code sketch to make the constraints concrete — this is not a proposal, just a way to ground the constraints
What ships with it
2 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.
- 1mo ago First seen · 77 lines · 62 tokens per session scan A 1c407bbba0bf
deep-module-refactor is a skill published in the GitHub repository zebbern/claude-code-guide (4,665 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 866 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
pull-request
Use when creating, reviewing, or responding to GitHub pull requests: opening a PR, addressing review feedback, resolving PR comments, running a code review, or getting a PR into a mergeable state. Triggers on phrases like 'open a PR', 'review this PR', 'address the comments', 'gh pr', or 'resolve review threads', even…
production-readiness-review
Audits any SaaS / web application codebase for production readiness and delivers a prioritized report covering security, scalability, reliability, performance, testing, observability, data, APIs, CI/CD, infrastructure, cost, and compliance. Use when the user asks to "make my project production-grade", wants a final…
witness
Independently witness that an Allium loop's convergence claim is true and was reached honestly. Use when the user wants to verify a loop's self-report, confirm tests really pass and no generated test was weakened, produce a convergence certificate or witness record, gate CI on a trustworthy signal, or check that an…
cdb-scan
Map this codebase into project memory — a code graph of every symbol and how they connect, plus a written profile of stack, layout, conventions and workflows. Re-run any time to refresh both in place. Use when memory is newly installed on an existing project, or when the project has changed enough that the stored map…
generic-fullstack-code-reviewer
Review full-stack code for bugs, security vulnerabilities, performance issues, accessibility gaps, and CLAUDE.md compliance. Enforces TypeScript strict mode, input validation, GPU-accelerated animations, and design system consistency. Use when completing features, before commits, or reviewing pull requests.
generic-code-reviewer
Review code for bugs, security vulnerabilities, performance issues, accessibility gaps, and CLAUDE.md workflow compliance. Supports any tech stack - HTML/CSS/JS, React, TypeScript, Node.js, Python, NestJS, Next.js, and more. Use when completing features, before commits, or reviewing pull requests.