feature-discovery

Scan AI/agent market developments over seven days and propose strategic product upgrades.

12|5|Updated Feb 5, 2026

Search Tags:#ai agents #product strategy #market research #competitive analysis #product roadmap #feature discovery

Ready to run

npx skills add https://github.com/davidahmann/gait --skill feature-discovery

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: feature-discovery
Source: https://github.com/davidahmann/gait/tree/main/.agents/skills/feature-discovery
Command: npx skills add https://github.com/davidahmann/gait --skill feature-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of staying ahead in the rapidly evolving AI and agent landscape by systematically identifying and proposing high-leverage strategic product upgrades for Gait.

Core Features & Use Cases

Market Scanning: Analyzes AI/agent developments over a defined 7-day window.
Strategic Prioritization: Filters for market shifts that materially impact Gait's competitive advantage and enterprise adoption.
Output Generation: Proposes up to 5 strategic upgrade features with evidence-backed justification.
Use Case: When asked to identify the next big strategic move for Gait, this Skill will scan recent AI news, identify a significant trend in agent orchestration, and propose a new feature that enhances Gait's control plane, backed by links to the relevant announcements.

Quick Start

Use the feature-discovery skill to scan the last 7 days and propose only high-leverage strategic upgrades.

Frequently Asked Questions about feature-discovery

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

FAQPage Schema

How do I identify high-leverage AI agent features for my product roadmap?▼

AI agent feature discovery requires systematic market scanning to identify material shifts in agent orchestration, filtering results for defensibility and enterprise adoption leverage to propose strategic upgrades.

What is the best way to perform competitive analysis on recent AI agent market trends?▼

Competitive analysis on AI agent trends works best by scanning a defined time window for material market shifts, filtering for differentiation and product relevance, and citing credible primary sources to justify strategic moves.

How does evidence-backed market scanning prioritize proposed product upgrades?▼

Evidence-backed market scanning prioritizes product upgrades by filtering identified AI agent trends through materiality, differentiation, defensibility, and adoption leverage to output up to five high-impact strategic features.

Can I use this market scanning approach for enterprise adoption and policy enforcement features?▼

Yes, this market scanning approach targets enterprise adoption by specifically evaluating AI agent shifts impacting policy enforcement, provenance, durable runtime, and fail-closed execution to propose relevant product upgrades.

How do I start a 7-day market scan to propose strategic product upgrades?▼

To start a 7-day market scan, define the AI agent landscape scope, then systematically evaluate recent news and announcements against criteria like materiality, differentiation, and product relevance to generate strategic upgrade proposals.

What are the limitations of using automated market scanning for product strategy?▼

Automated market scanning for product strategy is limited by its defined time window and requires strict filtering for materiality and defensibility to avoid proposing low-leverage features that lack credible primary source justification.