ANTIGRAVITY AWESOME SKILLS V9.4.0 HARDENS THE AGENTIC CODING STACK
The Antigravity Awesome Skills library shipped v9.4.0 focused on validation, CI guardrails, and marketplace sync reliability instead of new skills. The release...
The Antigravity Awesome Skills library shipped v9.4.0 focused on validation, CI guardrails, and marketplace sync reliability instead of new skills.
The release v9.4.0 tightens frontmatter parsing and plugin/bundle checks, adds dependency-review and actionlint workflows (with shellcheck-safe fixes), makes editorial bundle publication more atomic, refreshes credits, and clarifies maintainer merge policy.
That stability underpins how teams scale agents: multi-agent critique/planner patterns highlighted in these videos (argue/adversarial, beyond one agent), cost-and-quality guardrails from a practical Antigravity workflow token tiers, in-repo plans, debt gates, and self-optimizing scaffolds like MiniMax’s M2.7 self-refactoring loop.
If you’re experimenting with personal agents, this hands-on build shows the current stack in practice Claude Code + Gemini + Antigravity.
Safer validation and CI checks reduce breakage when publishing or updating agent skills and bundles.
Solid scaffolding lets you evaluate multi-agent and self-optimizing patterns without chasing flaky tooling.
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terminal
Run your private skills/bundles through v9.4.0 validators and add dependency-review/actionlint to CI; compare pre/post failures and publish success rate.
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terminal
A/B single-agent vs. planner+critic (adversarial) runs on a real task; track success rate, latency, and token cost using the token-tiering workflow.
Legacy codebase integration strategies...
- 01.
Adopt the in-repo Idea/Plan/Done/Debt docs and a quality gate to block unapproved shortcuts from release.
- 02.
Pin v9.4.0, enable new CI guardrails, and validate existing marketplace metadata to prevent regressions.
Fresh architecture paradigms...
- 01.
Design for multi-agent from day one (planner, implementer, critic) and log self-critique to memory between runs.
- 02.
Use token-tiering: reserve top models for planning/security reviews; use cheaper models for linting and boilerplate.