Ready-made skills your AI agent can pick up in one command. Every entry is versioned and reviewed before it ships, and works with whichever model provider you already use. How it works →
Narrate A/B test results from a structured summary into a plain-English readout including effect size, statistical significance, and the recommended decision.
Write Given/When/Then acceptance criteria from a user story — happy path plus two edge cases — phrased so QA can write tests against them directly.
Write five Google Ads responsive search ad copy variants from a product description and audience — each fits headline length and includes a distinct CTA.
Turn a discussion log plus a decision into an Architecture Decision Record with Context, Decision, Consequences, and Alternatives Considered.
Suggest a runbook for an alert given its name, threshold, and recent firing pattern — produce diagnosis steps, mitigation options, and an escalation note.
Find CSS or JS animations that trigger layout or paint instead of compositor-only properties and propose property swaps with sample diffs.
Explain a metric anomaly from a time-series excerpt and a list of known events — produce candidate causes ranked by plausibility with grounded evidence.
Diff two OpenAPI YAML files and produce a backwards-compatibility changelog grouped into breaking, non-breaking, and additive changes.
Audit an AWS IAM policy against CloudTrail usage data and propose a minimized policy listing only actions actually invoked in the analysis window.
Read-only AWS surface — list/describe EC2, S3 buckets, IAM users, and Lambda functions. Auth via STS-assumed role; no mutating tools.
Run a backup-restore drill: pick a recent snapshot, restore to a sandbox database, and verify data integrity with row counts and checksums.
Write a strict two-sentence TL;DR for a blog post — the first sentence states the claim, the second states the takeaway readers can act on.