seed-workflow

Automate instamolt.app seeding workflows across phases with operator-confirmed gates.

Updated Apr 9, 2026

Search Tags:#workflow #seeding #ai-agents #gatekeeping #seed

Ready to run

npx skills add https://github.com/lgoodcode/instamolt-seeder --skill seed-workflow-lgoodcode

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: seed-workflow
Source: https://github.com/lgoodcode/instamolt-seeder/tree/main/.claude/skills/seed-workflow
Command: npx skills add https://github.com/lgoodcode/instamolt-seeder --skill seed-workflow-lgoodcode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates an end-to-end seeding workflow for instamolt.app, coordinating seed-personas, generate, review, publish, and engage phases with explicit human-review gates.

Core Features & Use Cases

Interactive four-phase pipeline from personas to engagement, with surgical delete-and-regenerate when needed.
Enforced decision gates between phases to ensure quality and safety before progressing.
Resumable state management that allows easy continuation after interruptions and safe rollback paths.

Quick Start

Start the guided seeding workflow and proceed through pre-flight, phase reviews, and production ramp with explicit operator confirmations at each gate.

Frequently Asked Questions about seed-workflow

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

FAQPage Schema

How do I automate a safe AI agent seeding workflow with human review gates?▼

An automated AI agent seeding workflow coordinates generate, review, publish, and engage phases with enforced decision gates. It requires explicit operator confirmation between phases to ensure safe, repeatable deployments before progressing to wave-based scaling.

What is wave-based scaling for top-up seeding scenarios?▼

Wave-based scaling for top-up seeding is a controlled deployment method that targets specific agent and post counts. It applies surgical delete-and-regenerate fixes during generation phases while maintaining resumable state management to safely continue after interruptions.

How do I resume an interrupted seeding workflow after a failed deployment phase?▼

To resume an interrupted seeding workflow, the state management system tracks progress across pre-flight, phase reviews, and production ramp. This allows easy continuation after interruptions and provides safe rollback paths to previous checkpoints without losing completed work.

Can I delete and regenerate specific AI agents without restarting the entire pipeline?▼

Yes, surgical delete-and-regenerate functionality allows targeted removal and recreation of specific agents during the review phase. This isolated correction mechanism works within the four-phase pipeline without requiring a full restart of the seeding workflow.

What are the prerequisites for running a gated end-to-end AI seeding workflow?▼

Running a gated AI seeding workflow requires an instamolt.app environment configured for bootstrap or top-up scenarios. Operators must be available to provide explicit confirmation at each decision gate, ensuring quality control before wave-based scaling to target agent counts.

When should I use explicit checkpoints in an AI content generation pipeline?▼

Explicit checkpoints should be used in AI content pipelines when transitioning between generate, review, publish, and engage phases. They enforce operator confirmation and documented procedures, preventing unsafe deployments during wave-based scaling or surgical fixes.