Your AI writes code. AIContext teaches it to build features.
Most AI coding sessions lose context, skip planning, and need constant hand-holding. AIContext gives your AI assistant a complete development methodology — it interviews you before coding, creates a plan, executes it step by step with automated reviews and tests, and picks up exactly where it left off when you start a new session.
Works with any language or framework — PHP, Python, JavaScript, TypeScript, Rust, Go, and more.
Supports multiple AI tools — Claude Code, Codex, Cursor, and GitHub Copilot.
npm install -g @zahardev/aicontext
cd /path/to/your-project
aicontext initThen start a session: type /start (Claude Code) or use start (Codex, Cursor, Copilot). The AI will analyze your codebase and generate project context automatically.
Run /aic-help (or use aic-help) for a guided tour of available workflows and best practices.
Writing a CLAUDE.md or .cursorrules file gives your AI memory. AIContext gives it a way of working — built on Spec Driven Development, where the spec is the source of truth and code is derived from it:
/start-feature → Interview → Spec + Task(s)
↓
/run-task → Implement + Review + Test (automated per step)
↓
/finish-task → Sync docs, update worklog, handle git
The AI interviews you before writing code — exploring your codebase to avoid asking what it can determine itself. It recommends answers based on what it found, walks every dimension breadth-first so nothing is missed, and captures decisions as it goes. You confirm or correct — not explain from scratch.
The AI executes the plan — each step is implemented, reviewed, and tested automatically. You supervise rather than drive.
The AI reviews its own code — built-in review catches bugs, security issues, and architectural problems before you even look at the diff.
The AI tests in the browser — /web-inspect opens real pages, checks console errors, interacts with elements, and captures screenshots. No more copy-pasting console errors.
The AI drives the process — after every action, the AI tells you what to do next. Finished a step? "Run /next-step to continue." Closed a task? "Spec has more pending tasks — start the next one?" You never have to guess the next command.
The AI adapts to your workflow — on first run, it asks how you like to work: reviews after every step or only at the end? Commit per step or per task? Push automatically? It remembers your answers and never asks again.
The AI remembers across sessions — specs, tasks, and briefs capture everything. Start a new session, run /check-task, and the AI picks up where it left off. No knowledge is lost.
| Layer | What it captures | Example |
|---|---|---|
| Spec | What to build and why — requirements, decisions, non-goals | "Users can reset passwords via email. Not supporting SMS." |
| Task | How to build it — step-by-step plan with checkboxes | "Step 1: Add reset endpoint. Step 2: Email template. Step 3: Token expiry." |
| Brief | What the AI learned while building — patterns, gotchas, file references | "Auth middleware checks token in header, not cookie. See src/auth.js:42." |
Specs and tasks are committed to git. Briefs are gitignored — each developer accumulates their own working knowledge.
Learn more in the development model guide.
/start-feature— thorough discovery interview before any code is written/create-task— quick task creation from conversation when a full interview isn't needed/plan-tasks— break an existing spec into multiple tasks/add-idea— capture a deferred idea to the worklog mid-session so it's not lost
/run-task— execute all steps with built-in review and test loops/run-step— execute a single step with full control/do-it— turn a conversation into a task step and implement it immediately
/review— quick correctness scan (bugs, security, edge cases)/deep-review— comprehensive architecture + correctness + codebase health review- Specialized reviewer agent runs in parallel without consuming your main conversation (Claude Code)
/check-task— read spec, brief, and task to resume exactly where you left off/finish-task— close out a task: sync spec, write completion notes, handle git/align-context— sync all context files with current state
/draft-issue— draft a GitHub issue from conversation context, optionally create it on GitHub directly/draft-pr— generate PR description from task context and git history/gh-review-fix-loop— automate the review-fix-push cycle (works with CodeRabbit, human reviewers, etc.)/gh-fix-tests— fix failing CI checks automatically: diagnose, fix, push, retry until green
/interview— structured discovery on any topic — the AI walks dimensions, recommends answers, and captures decisions/brainstorm— generate missing angles, better implementations, and new combinations/thoughts— quick "what do you think?" check-in for feedback mid-conversation
/web-inspect— open pages, check console errors, interact with elements, capture screenshots
- Blocks destructive commands, enforces TDD, requires explicit permission before implementation
- Configurable quality checks: what runs after each step vs after the whole task — adapts to your preferences
See the full skills reference for detailed descriptions of all skills.
AIContext creates a .aicontext/ directory with shared rules, prompts, and templates. Each AI tool gets a thin entry point that loads this shared context:
| Tool | How to invoke skills |
|---|---|
| Claude Code | /skill-name (e.g., /start-feature) |
| Codex, Cursor, Copilot | use skill-name (e.g., use start-feature) |
Claude Code gets the richest experience with /command skills and parallel subagents. All tools share the same underlying prompts and instructions.
npm install -g @zahardev/aicontext
cd /path/to/your-project
aicontext initOr use npx for one-time setup: npx @zahardev/aicontext init
Clone the GitHub repository and copy the files you need:
git clone https://github.com/zahardev/aicontext.git /tmp/aicontext
cd /path/to/your-project
cp -r /tmp/aicontext/.aicontext .
cp -r /tmp/aicontext/.claude . # Claude Code
cp -r /tmp/aicontext/.codex . # Codex
cp -r /tmp/aicontext/.cursor . # Cursor
cp -r /tmp/aicontext/.github . # GitHub Copilot
rm -rf /tmp/aicontextaicontext update # Update framework files (preserves your project-specific files)
aicontext upgrade # Upgrade the CLI tool itself| Committed to git | Gitignored |
|---|---|
| Rules, prompts, templates, specs, tasks | Briefs, reviews, PR drafts, personal settings |
Team members share the same rules and task history. Each person's briefs and preferences stay local. See project structure for details.
One config file controls how the AI works — no prompt engineering needed:
- Project settings: Edit
.aicontext/config.yml(review/test/commit behavior, task naming, update checks) - Personal overrides: Create
.aicontext/config.local.yml(gitignored, overrides shared settings) - Team rules: Edit
.aicontext/project.md - Personal rules: Edit
.aicontext/local.md(gitignored) - Remove unused tools: Delete
.cursor/,.codex/,.github/, or.claude/as needed
- Development Model — how specs, tasks, briefs, and quality checks work together
- Workflow Guide — step-by-step guides for common workflows
- Skills Reference — detailed descriptions of every skill
| Version | Highlights |
|---|---|
| 1.7.0 | Adaptive workflow — the AI learns your preferences and stops asking. GitHub issue creation. Thinking tools (/interview, /brainstorm, /thoughts). Automated CI fix (/gh-fix-tests). Ideas backlog. Smarter interviews that recommend answers. |
| 1.6.0 | The big workflow release — three-layer context (spec/task/brief), structured planning, automated execution with review and test loops, PR automation, browser inspection with /web-inspect. |
| 1.5.0 | Codex support, /draft-issue, tool-agnostic PR scripts. |
| 1.4.0 | Slash command skills, PR workflow scripts, agent model upgrades to sonnet/opus. |
| 1.3.0 | Claude Code subagents — reviewer, researcher, test-runner working in parallel. |
| 1.2.0 | Auto-update checking, aicontext upgrade, .ai/ → .aicontext/ rename. |
| 1.0.0 | Initial release — rules, prompts, templates, multi-tool support. |
See CHANGELOG.md for full details.
MIT
