ANTIGRAVITY AWESOME SKILLS V8.8 SHIPS REVIEW-AND-OPTIMIZE PR AUTOMATION PLUS GOVERNANCE AND RESEARCH SKILLS
Antigravity Awesome Skills v8.8 adds review-and-optimize PR automation and two new skills for governance audits and equity research. The v8.8.0 release package...
Antigravity Awesome Skills v8.8 adds review-and-optimize PR automation and two new skills for governance audits and equity research.
The v8.8.0 release packages a post-8.7.1 merge batch and keeps the registry aligned at 1,311+ indexed skills. It introduces aegisops-ai for SDLC governance checks like Terraform cost drift and Kubernetes policy hardening, and xvary-stock-research for thesis-driven equity analysis using public SEC EDGAR data release notes.
Maintainers get a faster PR loop via a skill-review-and-optimize workflow and /apply-optimize to apply accepted suggestions from PR comments. Catalog metadata and docs were refreshed after the merge, covering Claude Code, Cursor, Codex CLI, Gemini CLI, and Antigravity users details.
Governance checks like Terraform cost drift and Kubernetes policy hardening can run inside AI-assisted workflows, tightening review loops without new in-house tooling.
PR review-and-optimize with comment-driven apply reduces toil for maintainers and speeds contribution cycles.
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terminal
Run aegisops-ai on representative Terraform modules and cluster policies; track false positives, runtime, and drift detection accuracy against known baselines.
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Dry-run the skill-review-and-optimize workflow in a sandbox repo and validate that /apply-optimize produces minimal, auditable diffs.
Legacy codebase integration strategies...
- 01.
Pilot the new workflow on a subset of repos; gate /apply-optimize behind labels and required reviewer to keep change control intact.
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Check GitHub app/bot permissions and org policies for PR comment-triggered actions, and audit any data egress from research skills.
Fresh architecture paradigms...
- 01.
Bake governance audits into repo templates as required checks with clear SLOs for runtime and signal-to-noise.
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Standardize contribution guides around the review-and-optimize loop so contributors get deterministic, automatable feedback.