crewai-setup
CrewAI multi-agent orchestration setup for collaborative AI systems
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance
dp-pattern-library
Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.
dp-optimizer
Apply advanced DP optimizations automatically
code-profiler
Profile code performance and identify bottlenecks
rag-chunking-strategy
Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking
closed-book-frontier-reasoning
Perform closed-book frontier reasoning — complex problem solving from internalized knowledge without external retrieval or tool use.
qdrant-integration
Qdrant vector database with filtering, payloads, and quantization support
prompt-template-design
Structured prompt template creation with variables, formatting, and version control
prompt-injection-detector
Prompt injection detection and prevention for secure LLM applications
nemo-guardrails
NVIDIA NeMo Guardrails configuration for conversational safety and control
multi-turn-tool-use
Design agents for multi-turn tool use — sequential tool calls, result accumulation, error recovery, and complex task decomposition over multiple turns.
session-management
Manage agent sessions including initialization, handoffs, revival (seance), and persistent identity for Polecats and Crew agents.
guardrails-ai-setup
Guardrails AI validation framework setup for LLM applications. Implement input/output validation, safety checks, and structured output enforcement.
multi-app-orchestration
Orchestrate workflows across multiple applications and APIs — inter-app coordination, data handoff, and multi-system task completion.
milvus-integration
Milvus distributed vector database configuration for large-scale RAG applications
autonomous-research-engineering
Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.
data-structure-selector
Select optimal data structure based on operation requirements
code-template-manager
Manage and generate competitive programming templates
cses-tracker
Track progress through CSES Problem Set with structured learning
prompt-compression
Token-efficient prompt compression techniques for cost optimization
medical-agent
Build medical AI agents for clinical decision support, medical record summarization, diagnostic assistance, and healthcare workflow automation.
codeforces-api-client
Interface with Codeforces API for contest data, problem sets, and submissions
mcp-host-styling-integration
Integrates MCP App UI with host theming system. Applies host CSS variables, handles onhostcontextchanged, safe area insets, display mode detection, and fullscreen configuration.
combinatorics-calculator
Calculate combinatorial values with modular arithmetic
pinecone-integration
Pinecone vector database setup, configuration, and operations for RAG applications
mcp-tool-resource-pattern
Implements the core MCP Apps architectural pattern where a Tool declares _meta.ui.resourceUri referencing a registered Resource. Covers registerAppTool, registerAppResource, text fallback, structuredContent, and app-only helper tools.
pii-redaction
PII detection and redaction utilities for privacy-compliant conversational AI
verification
Verification-before-completion discipline ensuring all success criteria are met, tests pass, and reviews complete before declaring work done.
complexity-analyzer
Automated Big-O complexity analysis of code and algorithms. Performs static analysis of loop structures, recursive call trees, space complexity estimation, and amortized analysis with detailed derivation documents.
constitution-creation
Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.
background-job-processing
Implement reliable background job processing systems — queue management, retry policies, dead-letter handling, and distributed workers.
backend-async-processing
Implement backend async and background processing patterns — event-driven architectures, message queues, async task runners, and worker pools.
implementation-execution
Execute development tasks to build features, producing code, tests, and configuration artifacts that satisfy specification requirements and comply with constitution standards.
systematic-debugging
Structured debugging methodology using hypothesis-driven investigation, log analysis, and bisection to isolate and resolve defects.
test-case-generator
Generate comprehensive test cases including edge cases, stress tests, and counter-examples for algorithm correctness verification. Supports random generation, constraint-based generation, and brute force oracle comparison.
continuous-learning
Pattern extraction, confidence-scored evaluation, skill creation, organization, versioning, and cross-project export pipeline.
context-engineering
Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.
planning-design
Design technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.
memory-summarization
Conversation summarization for memory compression and context management
langgraph-hitl
Human-in-the-loop integration for LangGraph workflows with approval and intervention points
langgraph-checkpoint
LangGraph checkpoint and persistence configuration for stateful workflow management
langfuse-integration
LangFuse LLM observability integration for tracing, analytics, and cost tracking
haystack-pipeline
Haystack NLP pipeline configuration for document processing and QA
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
huggingface-classifier
Hugging Face transformer model fine-tuning and inference for intent classification
code-review-pipeline
Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.
langchain-chains
LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns
finishing-a-development-branch
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work.
langchain-tools
LangChain tool creation and integration utilities for agent systems
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