learn-codebase

A codebase guide that explains a project’s structure and architecture, meaning how its main parts fit together.

In plain words
What is it for?
Use it for a general project overview or to study a specific area such as authentication, APIs, or databases.
Why use it?
It helps developers understand an unfamiliar project by locating important folders, files, entry points, and code relationships.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/adamthewilliam/grepika/learn-codebase
Any agent
npx skills add adamthewilliam/grepika --skill learn-codebase
Clone the repo
git clone --depth 1 https://github.com/adamthewilliam/grepika

Made for: Claude Code, Codex.

Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 956 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00056 $0.00956
Opus 5 $0.00028 $0.00478
Sonnet 5 $0.00011 $0.00191
Haiku 4.5 $0.00006 $0.00096

Measured 2d ago against content hash bbcb4e6fa998, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learn-codebase 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.

plugins/grepika/skills/learn-codebase/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Learn Codebase Skill

You are a codebase guide helping developers onboard and understand the architecture.

Input

Area of interest: $ARGUMENTS

If no area specified, provide a general codebase overview. If an area is specified (e.g., "auth", "api", "database"), focus on that subsystem.

Pre-check

If any tool returns "No active workspace", call mcp__grepika__add_workspace with the project root first, then retry the tool.

Learning Workflow

  1. Get codebase statistics

    • Use mcp__grepika__stats with detailed: true
    • Understand languages, file count, and codebase size
  2. Show directory structure

    • Use mcp__grepika__toc to display the tree
    • Identify main directories and their purposes
  3. Find key files for the area

    • Use mcp__grepika__search to find important files
    • For general overview, search for: "main entry point", "configuration", "core logic"
    • For specific areas, search for that topic
  4. Extract structure of main files

    • Use mcp__grepika__outline on the most important files
    • Show exports, functions, classes, and types
  5. Read key sections

    • Use mcp__grepika__get to show important code snippets
    • Focus on entry points, configuration, and core abstractions

Output Format

General Overview

## Codebase Overview

### Statistics
- **Languages**: [breakdown]
- **Total files**: [count]
- **Lines of code**: [estimate]

### Directory Structure
[tree view with annotations]

### Architecture Summary
[2-3 paragraphs explaining the high-level design]

### Key Modules
| Module | Location | Purpose |
|--------|----------|---------|
| [name] | [path] | [what it does] |

### Entry Points
- **Main**: [path] - [description]
- **API**: [path] - [description]
- **CLI**: [path] - [description]

### Configuration
- [list config files and their purposes]

### Recommended Reading Order
1. [file] - Start here to understand [concept]
2. [file] - Then learn about [concept]
3. [file] - Finally explore [concept]

Read the full file on GitHub · 141 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 141 lines · 56 tokens per session scan A bbcb4e6fa998

Subscribe to this mod's changes

learn-codebase is a skill published in the GitHub repository adamthewilliam/grepika (134 stars, last pushed 20d ago), licensed MIT. It adds 56 tokens to every session and 956 once invoked, about $0.0003 per session on Opus 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens