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github-project-management
A skill for managing GitHub projects using AI swarm coordination, featuring automated issue creation, project board synchronization, and intelligent workflow execution for efficient operations.
ai-dev-orchestration
A meta-orchestrator skill for AI-assisted application development, providing a structured 5-phase workflow with behavioral guardrails and prompt templates to ensure systematic and quality-driven development processes.
pair-programming
An AI-assisted pair programming skill offering multiple collaboration modes, real-time verification, and quality monitoring. It supports TDD, debugging, refactoring, and learning within structured workflows.
theater-detection-audit
This skill performs comprehensive code audits to detect placeholder code, mock data, TODO markers, and incomplete implementations, ensuring quality and consistency in codebases.
agent-creator
A specialized AI skill for creating and optimizing AI agents using systematic workflows and 5-phase SOP methodology. It focuses on building production-ready agents for specific domains and multi-agent coordination.
pattern-detection
Detect patterns, anomalies, and trends in code and data. Use when identifying code smells, finding security vulnerabilities, or discovering recurring patterns. Handles regex patterns, AST analysis, and statistical anomaly detection.
eta
When a user asks how long a task will take, requests a time estimate, or before starting any non-trivial coding task, immediately run scripts/estimate_task.py to analyze the codebase scope and provide a data-driven time estimate. Show the estimate breakdown, risk factors, and checkpoint recommendations without asking.
.claude
Imported from https://github.com/Equilateral-AI/equilateral-agents-open-core.
github-workflow-automation
This Claude Skill automates GitHub workflows using AI swarm coordination, providing intelligent CI/CD pipelines, workflow orchestration, and repository management for adaptive and self-organizing GitHub operations.
building-chat-interfaces
Build AI chat interfaces with custom backends, authentication, and context injection. Use when integrating chat UI with AI agents, adding auth to chat, injecting user/page context, or implementing httpOnly cookie proxies. Covers ChatKitServer, useChatKit, and MCP auth patterns. NOT when building simple chatbots without persistence or custom agent integration.
streaming-llm-responses
Implement real-time streaming UI patterns for AI chat applications. Use when adding response lifecycle handlers, progress indicators, client effects, or thread state synchronization. Covers onResponseStart/End, onEffect, ProgressUpdateEvent, and client tools. NOT when building basic chat without real-time feedback.
evaluation
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
building-rag-systems
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Benchmark Manager
Create and manage AILANG eval benchmarks. Use when user asks to create benchmarks, fix benchmark issues, debug failing benchmarks, or analyze benchmark results.
validating-api-responses
Validate API responses against schemas to ensure contract compliance and data integrity. Use when ensuring API response correctness. Trigger with phrases like "validate responses", "check API responses", or "verify response format".
orchestrating-multi-agent-systems
Orchestrate multi-agent systems with handoffs, routing, and workflows across AI providers. Use when building complex AI systems requiring agent collaboration, task delegation, or workflow coordination. Trigger with phrases like "create multi-agent system", "orchestrate agents", or "coordinate agent workflows".
gcp-examples-expert
Provide production-ready Google Cloud code examples from official repositories including ADK samples, Genkit templates, Vertex AI notebooks, and Gemini patterns. Use when asked to "show ADK example" or "provide GCP starter kit".
datacommons-client
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
engineering-features-for-machine-learning
Create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features".
analyzing-system-throughput
Analyze and optimize system throughput including request handling, data processing, and resource utilization. Use when identifying capacity limits or evaluating scaling strategies. Trigger with phrases like "analyze throughput", "optimize capacity", or "identify bottlenecks".
splitting-datasets
Split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning".
encrypting-and-decrypting-data
Validate encryption implementations and cryptographic practices. Use when reviewing data security measures. Trigger with 'check encryption', 'validate crypto', or 'review security keys'.
molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
archiving-databases
Use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".