Prime: Load Project Context
SkillFiles & storagePrimes the agent with deep codebase understanding by analyzing structure, documentation, and key files. Use when starting work on a codebase, at the beginning of a session, or when you need a fast orientation before planning or implementing. Optionally pulls external task context from Jira issues and Confluence pages first.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Prime: Load Project Context skill
What this skill tells your AI
The instructions your AI receives, as published by coleam00/ai-native-starter-pack in .claude/skills/prime/SKILL.md and read by ahel’s review.
Objective
Build comprehensive understanding of the codebase by analyzing structure, documentation, and key files. If external task references are provided, load them first so the codebase analysis is anchored to the actual work.
Process
Step 0: Load External Context
Run this step BEFORE the codebase analysis. It accepts optional arguments: [jira-issue-keys] [confluence-page-ids].
- Jira keys may be a single key (
PROJ-12) or comma-separated (PROJ-12,PROJ-13). - Confluence page ids are numeric page ids.
If Jira issue keys are provided:
- Call
mcp__atlassian__getAccessibleAtlassianResourcesto obtain thecloudId. - For each Jira key, call
mcp__atlassian__getJiraIssuewith thatcloudId, the issue key, andresponseContentFormat: "markdown". - Treat the returned issue summary, description, and acceptance criteria as the task context for everything that follows.
If Confluence page ids are provided:
- Call
mcp__atlassian__getConfluencePagefor each page id withcontentFormat: "markdown"(use thecloudIdfrom above, fetching it viamcp__atlassian__getAccessibleAtlassianResourcesif it was not already retrieved). - Treat the returned page content as supporting context (specs, design docs, requirements).
If no arguments are provided: Skip this step entirely and proceed to Step 1.
Briefly summarize any external context loaded before continuing — this frames the rest of the priming.
1. Analyze Project Structure
List all tracked files:
!git ls-files
Show directory structure:
On Linux, run: tree -L 3 -I 'node_modules|__pycache__|.git|dist|build'
2. Read Core Documentation
- Read CLAUDE.md or similar global rules file
- Read README files at project root and major directories
- Read any architecture documentation
3. Identify Key Files
Based on the structure, identify and read:
- Main entry points (main.py, index.ts, app.py, etc.)
- Core configuration files (pyproject.toml, package.json, tsconfig.json)
- Key model/schema definitions
- Important service or controller files
4. Understand Current State
Check recent activity:
!git log -10 --oneline
Check current branch and status:
!git status
Output Report
Provide a concise summary covering:
External Task Context (if loaded)
- Jira issue(s): key, title, one-line goal, acceptance criteria
- Confluence page(s): title and what they specify
Project Overview
- Purpose and type of application
- Primary technologies and frameworks
- Current version/state
Architecture
- Overall structure and organization
- Key architectural patterns identified
- Important directories and their purposes
Tech Stack
- Languages and versions
- Frameworks and major libraries
- Build tools and package managers
- Testing frameworks
Core Principles
- Code style and conventions observed
- Documentation standards
- Testing approach
Current State
- Active branch
- Recent changes or development focus
- Any immediate observations or concerns
Make this summary easy to scan - use bullet points and clear headers.
Signals
- GitHub stars
- 69
- Forks
- 23
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
prime- Source
- github.com/coleam00/ai-native-starter-pack