sniper-flow

Orchestrate AI agent teams through structured project lifecycle phases.

8|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/virkt25/sniper --skill sniper-flow
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sniper-flow
Source: https://github.com/virkt25/sniper/tree/main/packages/core/skills/sniper-flow
Command: npx skills add https://github.com/virkt25/sniper --skill sniper-flow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the complex orchestration of AI agent teams through structured project lifecycle phases, ensuring efficient and coordinated software development from discovery to deployment.

Core Features & Use Cases

  • Protocol Execution: Manages multi-phase project workflows (e.g., full, feature, patch, hotfix).
  • Agent Orchestration: Composes and spawns agents based on layered configurations (base, mixins, knowledge, conventions, learnings).
  • Checkpointing & Recovery: Saves progress between phases and allows for resuming interrupted workflows.
  • Gatekeeping & Review: Implements quality gates and interactive user reviews to ensure phase outputs meet standards.
  • Use Case: Initiate a full SNIPER protocol to build a new software feature, allowing parallel agents to implement code, with automated reviews and checkpoints at each stage.

Quick Start

Execute the sniper-flow skill to start a new project using the 'full' protocol.

Frequently Asked Questions about sniper-flow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate AI agent teams for structured software development workflows?

You can orchestrate AI agent teams for software development workflows by using a protocol-driven execution engine that manages agent composition, spawn strategies, and phase-based lifecycle execution from discovery to deployment.

What is the best way to manage multi-phase project workflows with AI agents?

Managing multi-phase project workflows with AI agents is handled through built-in protocols like full, feature, patch, and hotfix, which structure execution and coordinate parallel agents across the project lifecycle.

How do I resume an interrupted AI agent workflow if a deployment process fails?

To resume an interrupted AI agent workflow, the execution engine uses checkpointing to save progress between phases, allowing you to recover and continue deterministic software development workflows from the last saved state.

How does gatekeeping work in AI agent project execution?

Gatekeeping in AI agent project execution works by implementing quality gates and interactive user reviews at phase boundaries, ensuring outputs meet standards before advancing to the next stage of the workflow.

Do I need custom protocols to automate AI agent composition for different project intents?

You do not need custom protocols to automate AI agent composition, as the execution engine supports auto-detection for common project intents and composes agents using layered base, mixin, and knowledge configurations.

When should I not use a protocol-driven execution engine for AI agent orchestration?

A protocol-driven execution engine for AI agent orchestration may not be suitable when a software development task lacks structured lifecycle phases or requires flexible, ad-hoc execution outside of deterministic checkpointing and gatekeeping constraints.