skills.rest
GrinRus
GrinRusCOMMUNITY
Explore all skills by this author →
GitHub

aidd-flow-state

Coordinate and persist active-stage, progress, tasklist, and stage-result state across AI-assisted workflows.

5|Updated Oct 9, 2025
Search Tags:#workflow#orchestration#progress#tasklist#stage-state#stage-result
One-click install
npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-flow-state
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aidd-flow-state
Source: https://github.com/GrinRus/ai_driven_dev/tree/main/skills/aidd-flow-state
Command: npx skills add https://github.com/GrinRus/ai_driven_dev --skill aidd-flow-state

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Coordinates and persists the canonical active-stage, progress, tasklist normalization, and stage-result lifecycle across stage boundaries in AI-assisted development workflows.

Core Features & Use Cases

  • Canonical shared Python entrypoints for set_active_stage, stage_result, progress checks, and status summaries to coordinate multi-stage workflows.
  • Enables stage-state orchestration for active-feature/state, progress tracking, and consistent stage-result artifacts across research, plan, implement, and QA phases.
  • Integrates with aidd-runtime components and reports to keep looped pipelines synchronized and observable.

Quick Start

Initialize and exercise the flow-state runtime by setting the active stage for a ticket and emitting a stage-result to advance the workflow.

Frequently Asked Questions about aidd-flow-state

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

FAQPage Schema
How do I persist workflow progress and active-stage state across multiple AI development phases?▼

You can persist workflow progress and active-stage state by using canonical Python entrypoints to coordinate and track the stage-result lifecycle across boundaries like research, plan, implement, and QA phases.

What is stage-driven workflow orchestration for AI-assisted development?▼

Stage-driven workflow orchestration coordinates the progression from idea to QA by normalizing tasklists and maintaining consistent state and traceability across every stage boundary in the pipeline.

How do I normalize a tasklist and track stage results in a multi-stage pipeline?▼

Normalize a tasklist and track stage results by invoking shared runtime entrypoints that set the active stage, emit stage-result artifacts, and report progress summaries to keep pipelines synchronized.

Does this flow-state orchestration approach work with aidd-runtime gates and config?▼

Yes, flow-state orchestration conforms to aidd-runtime entrypoints and integrates with aidd-runtime gates and config to keep looped pipelines synchronized and observable across stage transitions.

When do I need canonical stage-state coordination for my tasklist?▼

You need canonical stage-state coordination when advancing through multi-phase workflows and requiring consistent, traceable stage-result artifacts and progress tracking across stage boundaries.

Related Skills

Explore More Software Engineering Skills→
GrinRusGrinRus
5

aidd-core

Aggregate canonical Python entrypoints under skills/*/runtime for cross-skill navigation.

Community
Advanced
mayurpisemayurpise
39

jira-create

Create Epic, Stories, and Sub-tasks in Jira from a jira-export.md file via MCP-Jira.

Community
Advanced
tygwantygwan
1

phase

Coordinate project phases and track their status with automated updates.

Community
Advanced
GrinRusGrinRus
5

implement

Run the implement stage loop and emit a canonical stage result JSON.

Community
Advanced