AI Agent Skills Marketplace

Reviews your code for bugs, security vulnerabilities, logic errors, performance issues, and style violations. Organizes findings by severity and suggests fixes with code examples.

Writes conventional commit messages by analyzing your staged git changes. Detects commit type, scope, and breaking changes automatically.

Give AI agents the ability to trace decision chains, reconstruct causal sequences, and reason over complex event timelines spanning months or years.

Diagnoses why your project will not start. Checks runtime versions, dependencies, environment variables, databases, ports, and build artifacts systematically.

SEO optimizer and banned-word scanner for Chinese social media. Keyword optimization and advertising law compliance.

Generates user-facing changelogs from git history. Transforms developer commit messages into clear release notes that users actually understand.

Generates a complete, polished README.md by scanning your actual project structure, dependencies, and code.

Catches dangerous database migrations before they hit production. Reviews schema changes for locking hazards, data loss, missing rollbacks, and index issues across PostgreSQL, MySQL, and SQLite.

Writes clear pull request descriptions by analyzing your branch diff. Covers what changed, why, how, and what to test. Works with GitHub, GitLab, and Bitbucket.

Multi-platform content generator for Chinese social media platforms

Weekly content calendar generator for Chinese social media. 7-day topic plans with trends and SEO keywords.

Cross-platform content repurposer for Chinese social media. One piece of content, multiple platform-native versions.

Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.

Published AI benchmarks measure brains in jars. They test models in isolation or within a single reference harness — and then attribute all performance to the model. This skill teaches you to decompose agent performance into its two actual components: model capability and harness multiplier. The result is evaluations that predict real-world behavior instead of benchmark theater.

Builds the organizational memory schema your AI agent needs to answer why — capturing decision provenance, causal chains, and event context that embedding-based retrieval permanently discards.

RAG fails quietly. It retrieves documents, returns confident-looking answers, and misses the question entirely — because the question required connecting facts across documents, reasoning about sequence, or tracing causation. This skill gives you a five-question diagnostic checklist that classifies any failing query as either RAG-safe or structurally RAG-incompatible, then maps it to the specific failure pattern and the architectural fix that resolves it.