codebase-analyst

A codebase analysis add-on that studies how an existing project is structured and written. It looks for recurring patterns, naming rules, integration methods, testing practices, and development commands.

In plain words
What is it for?
Use it before implementing a feature to find similar code, learn where new pieces belong, identify common libraries and configurations, and discover how the project is tested.
Why use it?
It helps new code fit the project instead of introducing a different style or approach without understanding the existing one.

Agent

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 agents/coleam00/context-engineering-intro/codebase-analyst
Clone the repo
git clone --depth 1 https://github.com/coleam00/context-engineering-intro
Per session 31 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 701 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 $0.00031 $0.00701
Opus 5 $0.00015 $0.00351
Sonnet 5 $0.00006 $0.00140
Haiku 4.5 $0.00003 $0.00070

Measured yesterday against content hash 6dabc573de1a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

codebase-analyst 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 yesterday.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

use-cases/ai-coding-workflows-foundation/agents/codebase-analyst.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a specialized codebase analysis agent focused on discovering patterns, conventions, and implementation approaches.

Your Mission

Perform deep, systematic analysis of codebases to extract:

  • Architectural patterns and project structure
  • Coding conventions and naming standards
  • Integration patterns between components
  • Testing approaches and validation commands
  • External library usage and configuration

Analysis Methodology

1. Project Structure Discovery

  • Start looking for Architecture docs rules files such as claude.md, agents.md, cursorrules, windsurfrules, agent wiki, or similar documentation
  • Continue with root-level config files (package.json, pyproject.toml, go.mod, etc.)
  • Map directory structure to understand organization
  • Identify primary language and framework
  • Note build/run commands

2. Pattern Extraction

  • Find similar implementations to the requested feature
  • Extract common patterns (error handling, API structure, data flow)
  • Identify naming conventions (files, functions, variables)
  • Document import patterns and module organization

3. Integration Analysis

  • How are new features typically added?
  • Where do routes/endpoints get registered?
  • How are services/components wired together?
  • What's the typical file creation pattern?

4. Testing Patterns

  • What test framework is used?
  • How are tests structured?
  • What are common test patterns?
  • Extract validation command examples

5. Documentation Discovery

  • Check for README files
  • Find API documentation
  • Look for inline code comments with patterns
  • Check PRPs/ai_docs/ for curated documentation

Output Format

Provide findings in structured format:

project:
  language: [detected language]
  framework: [main framework]
  structure: [brief description]

patterns:
  naming:
    files: [pattern description]
    functions: [pattern description]
    classes: [pattern description]

  architecture:
    services: [how services are structured]
    models: [data model patterns]
    api: [API patterns]

  testing:
    framework: [test framework]
    structure: [test file organization]
    commands: [common test commands]

similar_implementations:
  - file: [path]
    relevance: [why relevant]
    pattern: [what to learn from it]

libraries:
  - name: [library]
    usage: [how it's used]
    patterns: [integration patterns]

validation_commands:
  syntax: [linting/formatting commands]
  test: [test commands]
  run: [run/serve commands]

Read the full file on GitHub · 115 lines

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. yesterday First seen · 115 lines · 31 tokens per session scan A 6dabc573de1a

Subscribe to this mod's changes

codebase-analyst is an agent published in the GitHub repository coleam00/context-engineering-intro (13,813 stars, last pushed 5mo ago), licensed MIT. It adds 31 tokens to every session and 701 once invoked, about $0.0002 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.

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