Results for “self-evolution”

This skill enables makepad-skills to self-improve continuously during development.

This skill enables makepad-skills to self-improve continuously during development.

This skill enables makepad-skills to self-improve continuously during development.

Continuously improves Makepad development skills by capturing reusable patterns, fixing errors, and validating content during development.

This skill enables makepad-skills to self-improve continuously during development.

Analyzes development cycle and quality metrics to identify recurring issues, then generates additive patches to skill instructions to prevent them.

Self-evolution and contribution system for makepad-skills. Use when capturing new patterns, correcting skill errors, contributing learnings, or setting up hooks for auto-triggered skill updates.

Analyzes runtime history to identify failures and inefficiencies, then autonomously writes improvements using a protocol-constrained evolution engine. Communicates with EvoMap Hub via a local Proxy mailbox.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

当个人希望提升自身在职业或社会中的不可替代性，以应对环境变化和不确定性时

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

PUA Pro extensions: self-evolution notes, compaction state continuity, KPI-style summaries, flavor switching, and feedback tools.

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

PUA Pro extensions: self-evolution notes, compaction state continuity, KPI-style summaries, flavor switching, and feedback tools.

Autonomously runs iterative experiment loops to optimize code against a measurable metric. Use when the user wants to improve execution time, memory usage, test pass rate, or any numeric performance goal across repeated experiments — NOT for one-shot bug fixes or simple code review.

当意识到当前行为模式可能导致不理想的未来，需要一种具体方法来激发改变动力时

当需要将个人学习、成长与价值创造整合为一个持续发展的系统时

AutoAugment: Learning Augmentation Strategies from Data

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.