aming-claw-hn-demo-during-work

Name: aming-claw-hn-demo-during-work
Availability: InStock
Author: Aming

Record timeline DAG evidence from AI subagents during governed work demonstrations.

26|5|Updated Feb 28, 2026

Search Tags:#subagents #evidence-collection #ai-observability #aming-claw #timeline-dag #observer-lanes

Ready to run

npx skills add https://github.com/amingclawdev/aming-claw --skill aming-claw-hn-demo-during-work

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aming-claw-hn-demo-during-work
Source: https://github.com/amingclawdev/aming-claw/tree/main/skills/aming-claw-hn-demo-during-work
Command: npx skills add https://github.com/amingclawdev/aming-claw --skill aming-claw-hn-demo-during-work

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of invisible AI subagents during implementation and provides structured guidance to collect and show evidence through a timeline DAG, observer lanes, and gate mechanisms.

Core Features & Use Cases

Timeline DAG capture: orchestrates event sequencing from dispatch to handoff.
Observer/subagent lanes: clarifies decision origins and artifact provenance.
Dispatch gates and evidence inspector: enforces safe, auditable handoffs.
Isolated worktrees and append-only evidence: preserves history for replay and verification.
Governance-ready demonstrations: supports requirement anchors and safe startup.

Quick Start

Open the HN demo workspace and start collecting timeline DAG evidence across observer and subagent lanes.

Frequently Asked Questions about aming-claw-hn-demo-during-work

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

FAQPage Schema

How do I make AI subagents observable during live execution?▼

To make AI subagents observable, you capture a timeline DAG of event sequencing from dispatch to handoff, clarifying decision origins and artifact provenance across observer lanes.

How do I collect verifiable evidence for governed AI workflows?▼

You collect verifiable evidence by enforcing dispatch gates and evidence inspector mechanisms, utilizing isolated worktrees and append-only records to preserve history for replay and verification.

What is a timeline DAG in AI observability and when do I need it?▼

A timeline DAG in AI observability structures event sequencing from dispatch to handoff across observer and subagent lanes. You need it when demonstrations require auditable, verifiable governance anchors and safe startup sequences.

Can I use isolated worktrees to preserve AI agent history for replay?▼

Yes, you can use isolated worktrees alongside append-only evidence collection to preserve history for replay and verification during governed AI demonstrations and handoffs.

How do I enforce safe startup sequences for auditable AI demonstrations?▼

You enforce safe startup sequences by applying required governance anchors and graph-query prerequisites, ensuring dispatch gates and evidence inspection are recorded and reviewed before subagent handoffs.

What are the limitations of using observer lanes for subagent evidence collection?▼

Observer lanes are constrained by their dependency on isolated worktrees and append-only evidence, requiring strict dispatch gates and graph-query prerequisites to maintain auditable, replayable governance demonstrations without missing required anchors.