Agentic Coding CLI 🚀

_A CLI tool to set up Cursor rules and tasks for AI-augmented engineering workflows_

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Overview

This CLI tool helps you quickly set up a comprehensive set of Cursor rules and task templates for agentic coding. Instead of manually creating rules files, simply run one command to copy a battle-tested collection of rules, actions, guides, tools, and task templates into your project.

The rules work as part of a complete agentic coding system:
- Rules (installed by this CLI) teach the AI how to build software
- Tasks (YAML files) tell the AI what to build next
- Code (your project) gets created and modified through this workflow

This creates a virtuous loop: Ideas become Tasks → Tasks invoke Rules → Rules change Code → passing code closes Tasks.

Installation

``bash npm install -g agentic-coding`

Or clone and link locally:`bash git clone cd agentic-coding npm link`

`Usage`

Navigate to any project directory and run:

`bash agentic-coding setup`

This will copy all rules and task templates to your current directory, creating:

`your-project/ ├── .cursor/ │ └── rules/ │ ├── actions/ # Step-by-step playbooks │ ├── behaviours/ # Always-on guardrails │ ├── guides/ # Deep-dive reference docs │ └── tools/ # External CLI/API cheat-sheets └── .tasks/ ├── 0-draft/ # Ideas being refined ├── now/ # Current sprint work ├── next/ # Prioritized backlog ├── later/ # Future considerations ├── done/ # Completed tasks └── README.md # Task system documentation`

`What's Included`

`$3`


Step-by-step playbooks that turn chat commands into deterministic procedures:
- Task execution workflows
- Debugging procedures  
- Code review checklists
$3

Always-on guardrails that keep AI output consistent:
- Code style and naming conventions
- Tone and communication guidelines
- Security best practices
$3

Deep-dive reference documentation:
- Library usage policies
- Architecture decisions
- Security guidelines
$3

Cheat-sheets for external CLIs and APIs:
- Common command patterns
- Configuration templates
- Integration guides
$3

Structured YAML templates for work organization:
- Folder structure for task lifecycle (draft → now → next → later → done)
- YAML schema for executable specifications
- Context-aware task definitions with acceptance criteria
Rule System Architecture
The rules are designed to work with Cursor's context system:
- Agent-Requested: AI decides when to include based on relevance
- Always: Universal guidance that applies to every interaction
- Auto-Attached: Path-specific rules that load automatically
YAML Task Board – Work Items That Explain Themselves
The rules work best when paired with a structured task system. A task is _both_ a ticket and an executable spec:

`yaml id: FEAT-102 # Immutable slug context: # ← Cursor loads these first (critical!) code: - src/features/workflowDesigner/… docs: - .cursor/guides/ui/confirmations.mdc work_steps: # Ordered, file-level instructions - step_id: 1 targets: - src/… instructions: Create DeleteConfirmationDialog… acceptance: # Proof that we're done automated: - command: "npm run test --silent" description: All unit tests green manual: - description: Dialog matches design in Figma link self_checklist: # "Did I …?" reminders - "No direct DOM manipulation"`

Because the schema is explicit, Cursor can:

1. Load context before editing (no blind changes) 2. Executework_steps sequentially via edit_file, run_terminal_cmd, etc. 3. Prove success by running the same commands you would script in CI

`$3`

Organize tasks in your project using a simple folder structure:

`.tasks/ ├── 0-draft/ # Ideas being refined ├── now/ # Current sprint work ├── next/ # Prioritized backlog ├── later/ # Future considerations └── done/ # Completed tasks`

Or simply update the status: field in each YAML file.

`$3`

1. Idea appears in chat: "We should support dark mode" 2. Dev runs/Generate Task → CREATE_TASK interviews them and emits THEM-201-dark-mode.yml into 0-draft/3. Team refines acceptance criteria; file moves tonext/4. Sprint starts. Agent (or dev) opens task and says "execute" →EXECUTE_TASKloads context, edits code, runs tests, ticks checklist 5. All checks pass, PR auto-opens with the diff; task file lands indone/6. Any new knowledge becomes a Guide so the next dev never repeats the research

Ideas become Tasks → Tasks invoke Rules → Rules change Code → passing code closes Tasks – a virtuous loop.

`Development`

To modify or extend the rules:

1. Edit files in the rules/directory 2. Test withagentic-coding setup` in a test project
3. Commit changes to version control

Contributing

1. Fork the repository
2. Create a feature branch
3. Add or modify rules in the appropriate category
4. Test the CLI tool
5. Submit a pull request

---

> Philosophy: Process should accelerate engineers, not handcuff them. These rules turn tacit knowledge into executable guidance, letting humans and AI collaborate more effectively.

Agentic Coding CLI 🚀

_A CLI tool to set up Cursor rules and tasks for AI-augmented engineering workflows_

---

Overview

This creates a virtuous loop: Ideas become Tasks → Tasks invoke Rules → Rules change Code → passing code closes Tasks.

Installation

``bash npm install -g agentic-coding`

Or clone and link locally:`bash git clone cd agentic-coding npm link`

`Usage`

Navigate to any project directory and run:

`bash agentic-coding setup`

This will copy all rules and task templates to your current directory, creating:

`What's Included`

`$3`


Step-by-step playbooks that turn chat commands into deterministic procedures:
- Task execution workflows
- Debugging procedures  
- Code review checklists
$3

Always-on guardrails that keep AI output consistent:
- Code style and naming conventions
- Tone and communication guidelines
- Security best practices
$3

Deep-dive reference documentation:
- Library usage policies
- Architecture decisions
- Security guidelines
$3

Cheat-sheets for external CLIs and APIs:
- Common command patterns
- Configuration templates
- Integration guides
$3

Structured YAML templates for work organization:
- Folder structure for task lifecycle (draft → now → next → later → done)
- YAML schema for executable specifications
- Context-aware task definitions with acceptance criteria
Rule System Architecture
The rules are designed to work with Cursor's context system:
- Agent-Requested: AI decides when to include based on relevance
- Always: Universal guidance that applies to every interaction
- Auto-Attached: Path-specific rules that load automatically
YAML Task Board – Work Items That Explain Themselves
The rules work best when paired with a structured task system. A task is _both_ a ticket and an executable spec:

Because the schema is explicit, Cursor can:

`$3`

Organize tasks in your project using a simple folder structure:

Or simply update the status: field in each YAML file.

`$3`

Ideas become Tasks → Tasks invoke Rules → Rules change Code → passing code closes Tasks – a virtuous loop.

`Development`

To modify or extend the rules:

1. Edit files in the rules/directory 2. Test withagentic-coding setup` in a test project
3. Commit changes to version control

Contributing

1. Fork the repository
2. Create a feature branch
3. Add or modify rules in the appropriate category
4. Test the CLI tool
5. Submit a pull request

---

> Philosophy: Process should accelerate engineers, not handcuff them. These rules turn tacit knowledge into executable guidance, letting humans and AI collaborate more effectively.