dspy-bootstrap-fewshot
Use for BootstrapFewShot, bootstrapped demonstrations, teacher-model demos, and low-data DSPy prompt optimization.
dspy-custom-module-design
Use for creating custom DSPy modules, extending dspy.Module, reusable components, stateful modules, serialization, and module testing.
dspy-debugging-observability
Use for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.
dspy-embedding-retrieval
Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.
dspy-evaluation-suite
Use for evaluating DSPy programs with Evaluate, answer_exact_match, SemanticF1, custom metrics, baselines, and program comparisons.
dspy-finetune-bootstrap
Use for BootstrapFinetune, fine-tuning DSPy models, teacher-student distillation, weight optimization, and lower-cost deployment.
dspy-gepa-reflective
Use for GEPA reflective optimization, ReAct agent optimization, feedback metrics, LLM reflection, and execution trajectories.
dspy-haystack-integration
Use for integrating DSPy with Haystack, optimizing Haystack prompts, improving retrieval pipelines, and extracting DSPy prompts.
dspy-mcp-tool-integration
Use for MCP tools with DSPy, Model Context Protocol servers, dspy.Tool.from_mcp_tool, and ReAct agents over MCP-compatible tools.
dspy-miprov2-optimizer
Use for MIPROv2, Bayesian optimization, instruction and demo tuning, and high-performance DSPy program optimization.
dspy-optimize-anything
Use for GEPA optimize_anything on text artifacts such as code, prompts, agent architectures, configs, and non-DSPy optimization targets.
dspy-optimizer-selection
Use to choose or compare DSPy optimizers including LabeledFewShot, BootstrapFewShot, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, and BetterTogether.
dspy-output-refinement-constraints
Use for dspy.Refine, dspy.BestOfN, output constraints, validation, reward functions, and iterative output refinement.
dspy-production-deployment
Use for deploying DSPy with save/load, configure_cache, restrict_pickle, track_usage, async execution, streaming, and production runtime controls.
dspy-rag-pipeline
Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.
dspy-react-agent-builder
Use for ReAct agents, tool-calling agents, dspy.ReAct, multi-step reasoning and acting, and GEPA agent optimization.
dspy-reasoning-modules
Use for DSPy reasoning modules including RLM, ProgramOfThought, CodeAct, Parallel, sandboxed execution, and long-context workflows.
dspy-signature-designer
Use for DSPy signatures, InputField, OutputField, typed inputs and outputs, signature classes, and Pydantic-style structured schemas.
dspy-simba-optimizer
Use for SIMBA optimization, mini-batch introspective optimization, self-reflective rules, stochastic ascent, and numeric-metric optimization.
codex-code-reviewer
Systematic code review workflow using zen mcp's codex tool. Use this skill when the user explicitly requests "use codex to check the code", "check if the recently generated code has any issues", or "check the code after each generation". The skill performs iterative review cycles - checking code quality, presenting issues to the user for approval, applying fixes, and re-checking until no issues remain or maximum iterations (5) are reached.
deep-gemini
Deep technical documentation generation workflow using zen mcp's clink and docgen tools. First uses clink to launch gemini CLI in WSL for code analysis, then uses docgen for structured document generation with complexity analysis. Specializes in documents requiring deep understanding of code logic, model architecture, or performance bottleneck analysis. Use when user requests "use gemini for deep analysis", "generate architecture analysis document", "analyze performance bottlenecks", "deeply understand code logic", or similar deep analysis tasks. Default output is .md format.
main-router
Intelligent skill router that analyzes user requests and automatically dispatches to the most appropriate skill(s) or zen-mcp tools. Routes to zen-chat for Q&A, zen-thinkdeep for deep problem investigation, codex-code-reviewer for code quality, simple-gemini for standard docs/tests, deep-gemini for deep analysis, or plan-down for planning. Use this skill proactively to interpret all user requests and determine the optimal execution path.
plan-down
Method clarity-driven planning workflow using zen-mcp tools (chat, planner, consensus). Phase 0 uses chat to judge if user provides clear implementation method. Four execution paths based on automation_mode × method clarity - Interactive/Automatic × Clear/Unclear. All paths converge at planner for task decomposition. Produces complete plan.md file. Use when user requests "create a plan", "generate plan.md", "use planner for planning", "help me with task decomposition", or similar planning tasks.
simple-gemini
Collaborative documentation and test code writing workflow using zen mcp's clink to launch gemini CLI session in WSL (via 'gemini' command) where all writing operations are executed. Use this skill when the user requests "use gemini to write test files", "use gemini to write documentation", "generate related test files", "generate an explanatory document", or similar document/test writing tasks. The gemini CLI session acts as the specialist writer, working with the main Claude model for context gathering, outline approval, and final review. For test code, codex CLI (also launched via clink) validates quality after gemini completes writing.
Linear
Managing Linear issues, projects, and teams. Use when working with Linear tasks, creating issues, updating status, querying projects, or managing team workflows.
mini-wiki
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crash-instrumentation
Set up crash instrumentation with actionable context. Use when configuring crash capture, error boundaries, or breadcrumb strategies.
instrumentation-planning
Plan what to measure in mobile apps. Use when starting observability, prioritizing instrumentation, or asking "what should I track?"
interaction-latency
Measure time from user tap to action completion. Use when tracking button response times, form submissions, add-to-cart, or any tap-triggered operation.
navigation-latency
Measure time from navigation tap to screen fully loaded and interactive. Use when tracking screen transitions, deep links, or tab switches.
network-tracing
Instrument API requests with spans and distributed tracing. Use when tracking request latency, correlating client-backend traces, or debugging API issues.
session-replay
Set up session replay for visual debugging. Use when implementing screen recording, replay features, or visual debugging tools.
symbolication-setup
Configure crash symbolication for readable stack traces. Use when setting up dSYMs (iOS), ProGuard/R8 mappings (Android), or source maps (React Native).
user-journey-tracking
Track user journeys with intent context and friction signals. Use when instrumenting onboarding, checkout, or any multi-step flow where you need to understand WHY users fail.
alpha
Alpha sample skill for local testing of install/list flows.
anthropics-official-pack
All official Claude skills from anthropics/skills.
beta
Beta sample skill used to test install, validate, and dependency-free workflows.
claude-content-pack
Content toolkit: research, writing, summarization, and insights.
claude-design-pack
Design toolkit: UI/UX, themes, brand guidelines, and generative art.
claude-dev-pack
Developer toolkit: build, test, deploy, and debug with curated skills.
claude-office-pack
Office toolkit: PDF/Word/PPT/Excel + internal comms.
echo-skill-inline
Second bundled skill (inline dependency) for testing skillsets.
gamma
Gamma sample skill for local discovery and CLI smoke tests.
hello-skill
Minimal example Skill for testing skild installs.
hello-skill-inline
Minimal bundled skill (inline dependency) for testing skillsets.
hello-skillset
Example Skillset that bundles two minimal skills for testing.
kitchen-sink-skillset
Example Skillset that demonstrates inline + GitHub + registry dependencies.
obra-superpowers-pack
All skills from obra/superpowers (skills/).
project-os
AI project OS for autonomous loop, automated orchestration, and rule-driven execution.
registry-dep-skill
Example registry skill used as a dependency for kitchen-sink skillset smoke tests.
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