deep-module-refactor
Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules.
Skill by zebbern
Chalk Labs
Need help deploying deep-module-refactor?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/zebbern-deep-module-refactor
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/deep-module-refactor
What's inside
- Skill name
deep-module-refactor- Description
- Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.
- License
- MIT
- Instructions
- 73 lines in SKILL.md · view on GitHub
Files (3)
- LICENSE
- REFERENCE.md
- SKILL.md
SKILL.md (first lines)
# 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](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 Show this to the user, then immediately proceed to Step 5. The user reads and thinks about the problem while the sub-agents work in parallel. ### 5. Design multiple interfaces Spawn 3+ sub-agents in parallel using the Agent tool. Each must produce a **radically different** interface for the deepened module. Prompt each sub-agent with a separate technical brief (file paths, coupling details, dependency category, what's being hidden). This brief is independent of the user-facing explanation in Step 4. Give each agent a different design constraint: - Agent 1: "Minimize the interface — aim for 1-3 entry points max" - Agent 2: "Maximize flexibility — support many use cases and extension" - Agent 3: "Optimize for the most common caller — make the default case trivial" - Agent 4 (if applicable): "Design around the ports & adapters pattern for cross-boundary dependencies" Each sub-agent outputs: 1. Interface signature (types, methods, params) 2. Usage example showing how callers use it 3. What complexity it hides internally 4. Dependency strategy (how deps are handled — see [REFERENCE.md](REFERENCE.md)) 5. Trade-offs Present designs sequentially, then compare them in prose. After comparing, give your own recommendation: which design you think is strongest and why. If elements from different designs would combine well, propose a hybrid. Be opinionated — the user wants a strong read, not just a menu. ### 6. User picks an interface (or accepts recommendation) ### 7. Create GitHub issue Create a refactor RFC as a GitHub issue using `gh issue create`. Use the template in [REFERENCE.md](REFERENCE.md). Do NOT ask the user to review before creating — just create it and share the URL.
From zebbern/claude-code-guide, MIT.
More from zebbern/claude-code-guide
- 0.746score
audit-flow
ArunJRK
Interactive system flow tracing across CODE, API, AUTH, DATA, NETWORK layers with SQLite persistence and Mermaid export.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.740score
three-best-practices
three-agent-skills
Three.js performance optimization and best practices guidelines.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.740score
r3f-best-practices
three-agent-skills
React Three Fiber (R3F) and Poimandres ecosystem best practices.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
project-sizing-guide
zebbern
Software project effort estimation assistant.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
database-scout
zebbern
Explore SQLite and PostgreSQL databases: list tables, inspect schemas (columns/types/constraints), preview data, generate Mermaid ER diagrams, and run safe…
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
data-viz-renderer
zebbern
Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
http-load-profiler
zebbern
Run stepped HTTP load tests with ab/wrk, ramping concurrency levels to collect p50/p90/p99 latency, detect performance inflection points, and recommend…
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
chart-image
zebbern
Generate publication-quality PNG chart images from data, supporting line, bar, area, candlestick, pie, and heatmap charts.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
code-to-diagram
zebbern
Analyze codebases and automatically generate architecture diagrams, flowcharts, and org charts.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
dataset-quality-audit
zebbern
Run comprehensive quality checks on tabular data (CSV/Excel/TSV/JSON), detecting missing values, duplicates, outliers, format issues, and type inconsistencies…
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
code-vuln-audit
zebbern
Scan code for security issues: dependency vulnerabilities (npm/pip audit), secret leaks (regex and entropy analysis), and OWASP anti-patterns like SQL…
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
log-error-digest
zebbern
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports.
SkillCFreshness: C★ 4.7KClaude Code +9
Signals
- Stars
- 4,678
- Forks
- 471
- Open issues
- 3
- Last commit (this folder)
- May 31, 2026 (5 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/zebbern/claude-code-guide
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from zebbern
- 0.693score
api-fuzzing
zebbern
API fuzzing for bug bounty.
SkillAFreshness: A★ 131Claude Code +9 - 0.662score
data-viz-renderer
zebbern
Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
code-to-diagram
zebbern
Analyze codebases and automatically generate architecture diagrams, flowcharts, and org charts.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
chart-image
zebbern
Generate publication-quality PNG chart images from data, supporting line, bar, area, candlestick, pie, and heatmap charts.
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
code-vuln-audit
zebbern
Scan code for security issues: dependency vulnerabilities (npm/pip audit), secret leaks (regex and entropy analysis), and OWASP anti-patterns like SQL…
SkillCFreshness: C★ 4.7KClaude Code +9 - 0.662score
database-scout
zebbern
Explore SQLite and PostgreSQL databases: list tables, inspect schemas (columns/types/constraints), preview data, generate Mermaid ER diagrams, and run safe…
SkillCFreshness: C★ 4.7KClaude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get deep-module-refactor running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
deep-module-refactor skill. Agent Mart. https://agentmart.dev/skills/zebbern-deep-module-refactor (last updated 2026-10-24)