Prime

Prime: Load Project Context

Objective

Build understanding of the codebase - either high-level overview or focused deep-dive based on whether a focus area argument is provided.

Usage

• /prime - High-level overview (architecture, patterns, structure)
• /prime "authentication system" - Deep-dive into specific area
• /prime "API endpoints" - Focus on particular functionality

Decision: Which Mode?

Check if focus area argument was provided:

• Arguments provided → Use FOCUSED mode
• No arguments → Use HIGH-LEVEL mode

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HIGH-LEVEL Mode (No Arguments)

Goal: Understand architecture, patterns, and structure with minimal token usage.

Step 1: Directory Structure

Use Bash to show directory tree (2 levels max):

tree -L 2 -I 'node_modules|__pycache__|.git|dist|build|.next|coverage|venv|.venv'

If tree not available, use ls or alternative.

Step 2: Count Files (Optional)

Only if in a git repo:

git ls-files | wc -l

Step 3: Read Minimal Documentation

Read at most 3 files:

1. Root README.md (if exists)
2. CLAUDE.md or .claude/config (if exists)
3. One config file: package.json OR pyproject.toml OR tsconfig.json OR Cargo.toml

DO NOT read:

• Implementation files (src/, lib/, etc.)
• Test files
• Subdirectory READMEs
• Multiple config files

Step 4: Check Git State (If Git Repo)

git status
git log -10 --oneline

Step 5: Internalize Context

Build mental model of the project:

• Purpose and type of application (from README)
• Primary technologies and frameworks (from config)
• Directory structure and organization
• Key directories and their likely purposes (inferred from names)
• Main architectural patterns (if obvious from structure)

DO NOT output this to CLI. Keep context in memory for answering questions.

Step 6: Output Completion

Output only:

Finished priming project.

No detailed report. Context is loaded and ready for use.

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FOCUSED Mode (With Argument)

Goal: Deep understanding of a specific area, ignore everything else.

Step 1: Extract Keywords

From the focus area argument, identify key search terms.

Example: "authentication system" → keywords: auth, authentication, login, session, user

Step 2: Find Relevant Files

Use Grep to search for files containing focus keywords:

grep -r "keyword1\|keyword2" --files-with-matches --include="*.py" --include="*.ts" --include="*.js"

Or use Glob for pattern matching:

**/*auth*/**
**/*login*/**

Step 3: Read All Relevant Files

• Read ALL files found in Step 2 that are directly related
• Read imported dependencies if critical to understanding
• Read related documentation
• Go deep into implementation details

No limit on file reading in focused mode.

Step 4: Analyze for Structural Blockers

Internalize understanding (keep in memory, don't output):

• Key functions/classes/components
• Main data structures or types
• Entry points and exports
• Dependencies & integrations
• Patterns & conventions
• Testing approach

Detect structural problems that would block implementation:

Inconsistent Patterns:

• Mixed architectural styles (e.g., some files use MVC, others use different pattern)
• Inconsistent naming conventions across files in the focus area
• Multiple ways of doing the same thing with no clear standard

Unclear Organization:

• Files misplaced (e.g., business logic in UI components, or vice versa)
• Unclear module boundaries or responsibilities
• Circular dependencies or tangled imports
• Missing separation of concerns

Implementation Blockers:

• No clear entry point or hook for the requested feature
• Conflicting patterns that make it unclear which approach to follow
• Missing architectural foundation (e.g., no state management for feature that needs it)
• Unclear how focus area integrates with rest of system

Step 5: Output Result

If structural blockers found:

Output detailed blocker report:

Finished priming. Found structural blockers for "[focus_area]":

**Inconsistent Patterns:**
- [Specific inconsistency with file examples]
- [Another inconsistency]

**Unclear Organization:**
- [Specific organizational issue with file examples]

**Implementation Blockers:**
- [Specific blocker that prevents implementation]

**Recommendation:** [Suggest refactoring or clarification needed before proceeding]

If NO structural blockers found:

Output only:

Finished priming focused on "[focus_area]".

DO NOT output detailed reports to CLI unless blockers are found. Context is loaded and ready for use.

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Tips

• High-level mode optimizes for minimal context usage - great for initial orientation
• Focused mode goes deep - use when planning work on a specific feature/area
• Focus arguments should be descriptive: "user authentication flow" not just "auth"
• Can run high-level first, then focused on specific areas as needed

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: giladresisi
• Source: giladresisi/ai-dev-env
• License: MIT

Install and usage instructions live in the source repository linked above.