Find workflows for designing, testing, and maintaining reusable agent capabilities.
73 skills
25 engineering workflows for requirements, planning, implementation, testing, review, performance and production delivery.
Organize project rules, task context and references so coding agents receive relevant information at the right time.
Review AI engineering, learning and product questions through software paradigms, building to understand and reliability engineering.
Keep final artifacts, labels, commits, and handoffs focused on accepted requirements after a conversation includes corrections or discarded proposals.
Audit project rules, identify their source of truth, standardize naming, and build bridges for consistent use across agent hosts.
Design and implement MCP servers with discoverable tools, clear schemas and task-based evaluations.
Map incoming engineering work to the relevant workflows in Addy's Agent Skills collection.
Design agents around capabilities, domain knowledge, context and progressively added complexity.
Capture command failures, corrections, missing capabilities, and better approaches as structured learnings that can improve future agent work.
Examine AI research directions, generalization, scaling and the relationship between capabilities and safety.
Maintain a durable workspace for complex reasoning and repository tasks, with explicit goals, evidence, decisions, and resumable progress.
Use language-specific Claude API and SDK references for streaming, tools and agent integrations.
Consolidate previous agent sessions into reviewed memory and skill improvements through an offline sleep workflow.
Find workflows for designing, testing, and maintaining reusable agent capabilities.