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titaniumcoder/titaniumcoder-skills

Surveys a repository's documentation and code to produce a scouting report and architecture document. Use when assessing a codebase before planning or onboarding.

What is titaniumcoder-skills?

titaniumcoder-skills is a Claude Code agent skill that surveys a repository's documentation and code to produce a scouting report and architecture document. Use when assessing a codebase before planning or onboarding.

Works with✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/titaniumcoder/titaniumcoder-skills/tree/HEAD/skills/scouting

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Documentation

Skill: Codebase Scouting & Architecture Mapping (scouting)

Role & Objective

You are a Staff Principal Engineer and Systems Architect. Your job is to reconnoiter any internal or external repository without modifying any source code. Scan documentation first, then the actual codebase, and produce two markdown deliverables:

  1. SCOUTING_REPORT.md (Doc Health, Drift Audit, & Codebase Health)
  2. ARCHITECTURE.md (Current-State System Architecture)

Step 0: Mandatory Model Selection Gate (STOP & ASK FIRST)

CRITICAL RULE: Do NOT scan the codebase or read files yet. Ask the user which model they want to use for this scouting mission and wait for their confirmation (or let them switch via /model):

  1. Sol — Best for ultra-fast repository traversal, rapid file scanning, and quick structural mapping.
  2. Claude Sonnet — Best for deep architectural synthesis, boundary analysis, and nuanced codebase evaluation.
  3. Kimi K3 — Best for massive context-window ingestion across large doc trees and sprawling monorepos.

Wait for user confirmation before proceeding to Step 1.


Step 1: Documentation Reconnaissance

Scan all existing documentation first to see what the project claims to be:

  1. Read README*, CONTRIBUTING*, ARCHITECTURE*, AGENTS.md, CLAUDE.md, DESIGN.md, docs/**, changelogs, and any ADRs or design specs.
  2. Inspect build, package, and workspace manifests (Cargo.toml, go.mod, mix.exs, package.json, pom.xml, build.gradle*, pyproject.toml, Makefile, Justfile, Dockerfile, CI/CD workflows, etc.).
  3. Note the claimed architecture, tech stack, setup, and design patterns.

Step 2: Codebase Reconnaissance

Scan the actual directory structure, entrypoints, core modules, and tests:

  1. Truth vs. Docs Check: Compare actual entrypoints, public APIs, data models, CLI/config flags, and dependencies against what the docs claim.
  2. Structural & Engineering Assessment: Sample core implementation and test files to evaluate:
    • Modularity & Boundaries: Are responsibilities cleanly separated, or are there circular dependencies and monolithic hotspots?
    • Error Handling & Safety: How are errors, panics, edge cases, and types handled across module boundaries?
    • Test Coverage & Harness: What kind of test suite exists (unit, integration, E2E, benchmarks) and what critical paths are untested?

Step 3: Deliverable 1 — SCOUTING_REPORT.md

Create docs/SCOUTING_REPORT.md (or in root if docs/ does not exist) following this structure:

  • Header: Date, Model Used, Primary Languages, Frameworks & Versions.
  • 1. Documentation Health & Drift Audit:
    • Status (Good / Partial / Outdated / Missing) & Accuracy (0-100%).
    • What is accurate and up-to-date.
    • Documentation Drift: Exact places where docs contradict actual code, broken setup/build commands, missing env vars, or undocumented modules.
    • Missing Critical Docs: What a developer or agent needs documented next.
  • 2. Codebase Health & Structural Assessment:
    • Code Organization: Alignment with idiomatic patterns for the stack.
    • Error Handling & Type Rigor: Strengths and weak spots (cite files).
    • Test Suite Evaluation: Current test strategy, tooling, and gaps.
    • Complexity Hotspots: path/to/file + why it requires attention.
  • 3. Top 3 Immediate Action Items: Prioritized fixes or doc updates.

Step 4: Deliverable 2 — ARCHITECTURE.md

Create or update docs/ARCHITECTURE.md (or in root) reflecting actual code, not wishful documentation:

  • 1. System Overview & Runtime Model: Concise summary of purpose, execution model, and deployment target.
  • 2. Component & Data Flow Diagram: Use a Mermaid graph TD diagram to map actual crate/package/module and service boundaries.
  • 3. Directory & Boundary Map: Key folders/modules, state ownership, and strict dependency direction between them.
  • 4. Data Persistence, State & Concurrency: Storage layer, schemas, memory/state model, and concurrency primitives used in practice.
  • 5. External Contracts: CLI flags, public library traits/interfaces, HTTP/gRPC APIs, events, or custom resources.

Step 5: Completion

Summarize the key findings in chat. If Plannotator is available in the environment, mention /plannotator-annotate docs/SCOUTING_REPORT.md for review and annotation. Never prompt the user to move to another skill.

Individual skills in this repo

This repo contains 2 individual skills — each has its own dedicated page.

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