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.

Detects generalizable learnings from problem-solving interactions and proposes skill updates to improve future performance.

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.

Continuously monitors system performance, identifies improvement opportunities, and orchestrates skill discovery and creation to autonomously evolve capabilities.

Analyzes past agent sessions to identify recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them, tracking improvement over time.

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.

Guides agents through the full lifecycle of NeMo-RL experiments: understanding recipes, launching reproducible runs, analyzing results, and preserving human oversight with git and TSV logs.

Compares Trailmark code graphs at two source code snapshots to surface security-relevant structural changes that text diffs miss, such as new attack paths, blast radius growth, and privilege boundary modifications.

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

Research-backed evolution advice for your knowledge system. Analyzes health reports, friction patterns, and derivation history to propose specific changes with research justification. Never auto-implements — proposals require your approval.

Evolution Agent

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

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

Expert in designing and optimizing growth loops - self-reinforcing systems where the output of one cycle becomes the input for the next. Covers viral loops, content loops, paid loops, sales loops, and product loops. Knows how to identify, measure, and accelerate the specific loop mechanics that drive sustainable growth. Use when "growth loop, viral loop, content loop, flywheel, compounding growth, acquisition loop, referral loop, " mentioned.

Optimize AI agents through automated evolution cycles using LLM-driven mutation of prompts, skills, and memory against measurable benchmarks.

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).

Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

Generates four improved variations of any installed skill, scores them against a weighted rubric, and applies the winning version while preserving the original.

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

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

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

Improve agent reliability over time — diagnose why agents fail and fix the setup. Triggers on: agent keeps failing, same mistake again, agent not improving, make agent smarter, agent quality plateau, agents ignore skills, agent skips tests, fix agent behavior, agent unreliable, improve agent setup, self-improving harness, agents worse over time, tune agent instructions, agent going in circles, agent ignores AGENTS.md, repeated agent errors. Requires harness v0 and eval harness. AUTO-ROUTED from harness-engineering on symptoms. Not first setup — harness-generation first.

Treats rough ideas as competing organisms on an evolution island, scoring, selecting, crossing, and evolving them through structured rounds to surface the strongest concepts.