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Found 23 Skills
Create, improve, and manage Droid skills. Use when the user wants to: - Create new skills from scratch or from session learnings - Improve existing skills based on user preferences - Analyze sessions to identify patterns worth codifying - Understand best practices for agentic skill design This is a meta-skill for self-improvement and continuous learning.
Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills in the right order at the right depth. Auto-activates on any coding task. Centralizes the decision logic for which skills to use, how deep each goes, and how artifacts pass between them. Handles three pipeline variants: standard (plan-interview, intent-framed-agent, context-surfing, simplify-and-harden, self-improvement), team-based (agent-teams-simplify-and-harden), and CI (simplify-and-harden-ci, self-improvement-ci). Use this skill whenever starting any coding work — it determines the appropriate pipeline depth and variant automatically. Does not replace individual skills; dispatches to them.
Continuous self-improvement through structured reflection and memory
Evolutionary self-improvement for Hermes Agent using DSPy + GEPA to optimize skills, prompts, and code
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.
Use when the user asks to "improve my agent", "self-improving agent", "auto-tune my agent", "iterate on my agent prompt", "fix my agent based on test results", "close the loop on agent quality", "auto-improve agent prompt", "use eval results to improve agent", "optimize my prompt based on failures", "rewrite my prompt", or describes agent self-improvement, prompt iteration from run results, or automated agent quality loops. Covers the full diagnose → propose → apply → re-validate loop for VAPI agents (squads + tool definitions) and for self-hosted agents (custom websocket servers, including the offline / pasted-prompt degenerate variant).
Start a repo-local OptimizeSpec self-improvement change. Use when the user wants to create evals, optimize an agent with GEPA, define an agent self-improvement loop, or begin an ASI-first evaluation workflow.
Add persistent learning and self-improvement to AI agents using ACE framework
Load this skill immediately when the user expresses any intent. System capabilities (tools/knowledge/scripts) live inside the plugin and are maintained through plugin updates. User data must live at project-level `.claude/pensieve/` and is never overwritten by the plugin. When the user asks to improve Pensieve system behavior (plugin content), you must use the Self-Improve tool (`tools/self-improve/_self-improve.md`).
Route a full Blazor work request across the appropriate specialist lane(s). Use when the request spans more than one concern (authoring, data, auth, review) or when lane selection itself is uncertain. Triggers on full-request phrasing: "implement this feature", "review and refactor this page", "build this form end-to-end". Distinct from blazor-component-architect (user-level, external, single-lane authoring guidance that may be invoked as a specialist resource).
Gain wisdom from setbacks — Go through the 5-step interactive reflection (Setback → Automatic Output → Old Weights → New Parameters → Alternative Action), move from "emotional review" to "behavioral training", and update the L3 weights of your first reactions. Use when Wang Jianshuo reflects on a personal setback, mistake, or recurring pattern (reflection, post-mortem review, review, draw lessons, learn from a setback, gain wisdom, "I messed up again", "Why does this keep happening?", "Why do I always…?", "I can't just let it go", "I know the principles but can't put them into practice"). For the user as a human, not for Claude's task post-mortems.