sparc-methodology
SPARC methodology for multi-agent development: systematic Specification, Pseudocode, Architecture, Refinement, and Completion workflows via Claude Flow orchestration.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
181 skills found
SPARC methodology for multi-agent development: systematic Specification, Pseudocode, Architecture, Refinement, and Completion workflows via Claude Flow orchestration.
Generates standardized metadata, including git/jj version info and timestamps, for research docs, handoffs, and implementation plans.
Capture and formalize software development ideas into structured design documents within the Hashbrown repository, including research and conceptual sketches.
Automated session cleanup and documentation tool. Proactively updates CLAUDE.md, detects automation patterns, extracts insights, and organizes pending tasks.
Perform cohort analysis on user engagement data. Identify retention trends, feature adoption rates, churn patterns, and generate actionable research recommendations through quantitative data analysis.
Supermemory is a long-term memory infrastructure for AI agents, enabling persistent context, user profiles, and semantic RAG across multi-modal knowledge bases.
Synthesize interview transcripts into a structured template including Jobs to Be Done (JTBD), satisfaction signals, and actionable items.
A comprehensive library of 305+ modular instruction packages, Python CLI tools, and agent workflows designed to extend the capabilities of AI coding assistants like Claude Code, Cursor, Aider, and Gemini CLI.
Discover and recommend combinations of agent skills to complete complex, multi-faceted tasks using Maximum Quality or Minimum Dependencies strategies.
Execute z.AI CLI for multimodal analysis, web search, reader, and GitHub repo exploration via CLI and MCP.
Enforce structured JSON output from Claude models using Bedrock tool_use to eliminate parsing failures and ensure schema compliance.
A framework for managing the end-to-end LLM project lifecycle, from evaluating task-model fit and pipeline architecture design to implementing structured output parsing and agent-assisted development.