Results for “recursive-self-improvement”

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 error recovery — detect failures, diagnose root cause, apply fixes, and resume without stopping

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

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.

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

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

This skill should be used when the user wants to "improve a skill", "update skill based on feedback", "optimize skill after use", or says "自更新", "/自更新". Collects user feedback after using a skill and automatically updates that skill with improvements.

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

Enforce code quality with Ruff in a controlled, iterative workflow: run checks with optional scope and rule overrides, apply safe and unsafe autofixes, review diffs, and resolve remaining findings.

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

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

当意识到自己处于被动、重复、缺乏意义的生活状态，想要主动设计人生时

Facilitate a structured sprint retrospective to surface insights and produce actionable improvements with owners and deadlines.

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

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

通用多轮迭代改进：对任意研究制品反复调用 /review → 解析反馈 → 修复 → 更新 wiki，直到达标

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Facilitates and documents a team retrospective capturing what went well, what to improve, and action items. Use at the end of sprints, projects, or milestones to reflect and improve team practices.

週次リフレクション（1週の振り返りサマリ）を生成するスキル。直近の完了週の日記から4要素（作業傾向/思考癖/繰り返す課題/CCアドバイス）を生成し、_INDEX気づきTop3はユーザー承認後に反映。ユーザーが「リフレクション」「今週の振り返り」「思考癖まとめて」と言った時、または /reflection を呼んだ時に発動。resume-sessionの検知提案からも起動される。

Analyzes evaluation patterns across completed plans and evolves the superpowers checklists accordingly.

当个体感到生活缺乏方向、陷入重复性任务或被动接受外部目标，导致焦虑、无聊或停滞时，调用此模型以重新获得对生活的掌控感和成长动力

Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.