🎯Skills21
Self-improvement skill that captures learnings, errors, and corrections to enable continuous improvement by logging failed operations, user corrections, and discovered better approaches to markdown files for later processing
An agent skill that captures learnings and errors with hook-based activation, enabling automatic skill extraction for continuous self-improvement in CI workflows.
An agent skill that uses multi-agent teams to simplify and harden code, combining collaborative agent workflows with code quality improvement.
An agent skill focused on simplifying and hardening code by reducing complexity and improving robustness in existing codebases.
An agent skill that runs a structured interview before planning non-trivial implementations, helping ensure thorough requirements gathering.
Intent-framed agent skill for structuring AI agent behavior using intent-based communication patterns.
CI simplification and hardening skill for streamlining and securing CI/CD pipelines. Provides patterns for reducing complexity while improving security.
An agent skill for querying DX Data Cloud to retrieve developer productivity metrics, DORA metrics, PR/deployment data, and engineering analytics.
Monitors context window health during multi-step agent sessions, detecting drift signals like hallucination and contradiction to ride peak context quality, then executes clean handoffs with structured state files when context degrades.
A pipeline orchestrator skill that classifies tasks and routes them through the right combination of agent skills at the right depth, tying together planning, verification, self-improvement, and quality review workflows.
A skill that turns repeated agent mistakes into durable rules that persist across sessions. It captures where the agent failed, identifies missing knowledge, and encodes corrections so the agent improves over time through an outer learning loop.
A skill from a philosophy-driven AI agent collection that focuses on exploiting peak context moments through structured workflows for planning, execution, verification, and self-improvement across sessions.
A philosophy-driven AI agent skill collection that exploits peak context moments during planning, execution, and completion, featuring structured interviews, intent tracking, verification gates, and cross-session learning loops.
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