# Prime

> Use when loading project context and architecture overview before starting implementation work, with optional focus on a specific area

- **Type:** Skill
- **Install:** `agentstack add skill-giladresisi-ai-dev-env-prime`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [giladresisi](https://agentstack.voostack.com/s/giladresisi)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [giladresisi](https://github.com/giladresisi)
- **Source:** https://github.com/giladresisi/ai-dev-env/tree/main/skills/prime

## Install

```sh
agentstack add skill-giladresisi-ai-dev-env-prime
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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**

---

## 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):
```bash
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:
```bash
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)

```bash
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.

---

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

Or use Glob for pattern matching:
```bash
**/*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.

---

## 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](https://github.com/giladresisi)
- **Source:** [giladresisi/ai-dev-env](https://github.com/giladresisi/ai-dev-env)
- **License:** MIT

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-giladresisi-ai-dev-env-prime
- Seller: https://agentstack.voostack.com/s/giladresisi
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.