review-rust
Comprehensive Rust code review that fans out across detected technology areas, running them in parallel when the agent supports subagents and sequentially otherwise. Use for pre-push or pre-PR review of .rs files.
rust-best-practices
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rust-code-review
Reviews Rust code for ownership, borrowing, lifetime, error handling, trait design, unsafe usage, and common mistakes. Use when reviewing .rs files, checking borrow checker issues, error handling patterns, or trait implementations. Covers Rust 2024 edition patterns and modern idioms.
rust-project-setup
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rust-testing-code-review
Reviews Rust test code for unit test patterns, integration test structure, async testing, mocking approaches, and property-based testing. Covers Rust 2024 edition changes including async fn in traits for mocks, #[expect] lint suppression, LazyLock test fixtures, and temporary scope changes affecting test assertions. Use when reviewing _test.rs files, #[cfg(test)] modules, or test infrastructure in Rust projects. Covers tokio::test, test fixtures, and assertion patterns.
serde-code-review
Reviews serde serialization code for derive patterns, enum representations, custom implementations, and common serialization bugs. Use when reviewing Rust code that uses serde, serde_json, toml, or any serde-based serialization format. Covers attribute macros, field renaming, and format-specific pitfalls.
sqlx-code-review
Reviews sqlx database code for compile-time query checking, connection pool management, migration patterns, and PostgreSQL-specific usage. Use when reviewing Rust code that uses sqlx, database queries, connection pools, or migrations. Covers offline mode, type mapping, and transaction patterns.
tokio-async-code-review
Reviews tokio async runtime usage for task management, sync primitives, channel patterns, and runtime configuration. Covers Rust 2024 edition changes including async fn in traits, RPIT lifetime capture, LazyLock, and if-let temporary scoping. Use when reviewing Rust code that uses tokio, async/await patterns, spawn, channels, or async synchronization. Also covers tokio-util, tower, and hyper integration patterns.
gen-test-plan
Analyze repo, detect stack, trace changes to user-facing entry points, generate E2E YAML test plan
run-test-plan
Execute YAML test plan, stop on first failure, output rich debug prompt
fetch-pr-feedback
Fetch unresolved review comments from a PR and evaluate with receive-feedback skill
fix-llm-artifacts
Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.
gen-release-notes
generate release notes for changes since a given tag
prompt-improver
Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.
respond-pr-feedback
Respond to review comments on a PR after evaluation and fixes
review-llm-artifacts
Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.
review-plan
Review implementation plans for parallelization, TDD, types, libraries, and security before execution
review-skill
Reviews PRs that add or modify Agent Skills, checking structural validity, design quality, and marketplace consistency. Use when reviewing skill file changes, auditing SKILL.md quality, or running automated skill PR reviews.
dspy-rag-pipeline
This skill should be used when the user asks to "build a RAG pipeline", "create retrieval augmented generation", "use ColBERTv2 in DSPy", "set up a retriever in DSPy", mentions "RAG with DSPy", "context retrieval", "multi-hop RAG", or needs to build a DSPy system that retrieves external knowledge to answer questions with grounded, factual responses.
dspy-output-refinement-constraints
This skill should be used when the user asks to "refine DSPy outputs", "enforce constraints", "use dspy.Refine", "select best output", "use dspy.BestOfN", mentions "output validation", "constraint checking", "multi-attempt generation", "reward function", or needs to improve output quality through iterative refinement or best-of-N selection with custom constraints.
dspy-simba-optimizer
This skill should be used when the user asks to "optimize with SIMBA", "use Bayesian optimization", "optimize agents with custom feedback", mentions "SIMBA optimizer", "mini-batch optimization", "statistical optimization", "lightweight optimizer", or needs an alternative to MIPROv2/GEPA for programs with rich feedback signals.
dspy-signature-designer
This skill should be used when the user asks to "create a DSPy signature", "define inputs and outputs", "design a signature", "use InputField or OutputField", "add type hints to DSPy", mentions "signature class", "type-safe DSPy", "Pydantic models in DSPy", or needs to define what a DSPy module should do with structured inputs and outputs.
dspy-react-agent-builder
This skill should be used when the user asks to "create a ReAct agent", "build an agent with tools", "implement tool-calling agent", "use dspy.ReAct", mentions "agent with tools", "reasoning and acting", "multi-step agent", "agent optimization with GEPA", or needs to build production agents that use tools to solve complex tasks.
dspy-gepa-reflective
This skill should be used when the user asks to "optimize an agent with GEPA", "use reflective optimization", "optimize ReAct agents", "provide feedback metrics", mentions "GEPA optimizer", "LLM reflection", "execution trajectories", "agentic systems optimization", or needs to optimize complex multi-step agents using textual feedback on execution traces.
dspy-advanced-module-composition
This skill should be used when the user asks to "compose DSPy modules", "use Ensemble optimizer", "combine multiple programs", "use dspy.MultiChainComparison", mentions "ensemble voting", "module composition", "sequential pipelines", or needs to build complex multi-module DSPy programs with ensemble patterns or multi-chain comparison.
skill-perfection
Use this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.
dspy-evaluation-suite
This skill should be used when the user asks to "evaluate a DSPy program", "test my DSPy module", "measure performance", "create evaluation metrics", "use answer_exact_match or SemanticF1", mentions "Evaluate class", "comparing programs", "establishing baselines", or needs to systematically test and measure DSPy program quality with custom or built-in metrics.
dspy-optimize-anything
Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more
dspy-miprov2-optimizer
This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).
dspy-haystack-integration
This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline optimization", "combining DSPy and Haystack", "extract DSPy prompt for Haystack", or wants to use DSPy's optimization capabilities to automatically improve prompts in existing Haystack pipelines.
dspy-bootstrap-fewshot
This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
dspy-debugging-observability
This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.
dspy-finetune-bootstrap
This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.
dspy-custom-module-design
This skill should be used when the user asks to "create custom DSPy module", "design a DSPy module", "extend dspy.Module", "build reusable DSPy component", mentions "custom module patterns", "module serialization", "stateful modules", "module testing", or needs to design production-quality custom DSPy modules with proper architecture, state management, and testing.
video-prompting
Draft and refine prompts for video generation models (text-to-video and image-to-video). Use when a user asks for a "video prompt" or a model-specific prompt such as Ovi, Sora, Veo 3, Wan 2.2, LTX-2, or LTX-2.3, including requests like "text-to-video prompt", "image-to-video prompt", or "write a prompt for [model]".
integrate-whatsapp
Connect WhatsApp to your product with Kapso: onboard customers with setup links, detect connections, receive events via webhooks, and send messages/templates/media. Also manage WhatsApp Flows (create/update/publish, data endpoints, encryption). Use when integrating WhatsApp end-to-end.
automate-whatsapp
Build WhatsApp automations with Kapso workflows: configure WhatsApp triggers, edit workflow graphs, manage executions, deploy functions, and use databases/integrations for state. Use when automating WhatsApp conversations and event handling.
observe-whatsapp
Observe and troubleshoot WhatsApp in Kapso: debug message delivery, inspect webhook deliveries/retries, triage API errors, and run health checks. Use when investigating production issues, message failures, or webhook delivery problems.
frontend-design-system
フロントエンドUIデザインを洗練された独自性のあるスタイルで生成します。ランディングページ、ダッシュボード、Webアプリケーションのデザイン、UIコンポーネント作成時に使用してください。「AIっぽい」汎用デザインを避け、プロフェッショナルで記憶に残るUIを実現します。
seer
Visual feedback capture for any running macOS app window via osascript plus screencapture/ffmpeg screen capture. Use when the user wants UI verification or a fresh screenshot.
odoo-upgrade
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frontend-js
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theme-snippets
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theme-design
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theme-scss
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theme-create
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odoo-docker
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odoo-i18n
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odoo-report
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odoo-security
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