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Found 9 Skills
Patterns for building AI agents that learn from their own execution, detect failure modes, and improve autonomously. Covers feedback loops, performance regression detection, memory curation, skill extraction, and meta-learning architectures. Use when building agents that need to get better over time, managing auto-memory, or designing self-correcting systems.
Synthesize user feedback from multiple channels and identify patterns to inform product decisions. Use when analyzing feedback, prioritizing feature requests, conducting NPS surveys, or understanding user sentiment. Covers feedback collection, categorization, prioritization frameworks, and closing the feedback loop.
Guide competency framework development and operation. Use when building training that produces capability, when existing training doesn't produce competence, when structuring knowledge for multiple audiences, or when setting up feedback loops to surface gaps.
Analyze user/customer feedback and produce a User Feedback Analysis Pack (source inventory, normalized feedback table, taxonomy/codebook, themes + evidence, recommendations, and feedback loop). Use for voice of customer, feature request analysis, support ticket synthesis, churn reason synthesis, and survey open-ends.
Execute written implementation plans: first read and critically review the plan, then implement in small batches (default 3 tasks), produce verification evidence per batch and pause for feedback; must stop immediately and ask for help when blocked/tests fail/plan unclear. Trigger words: execute plan, implement plan, batch execution, follow the plan.
Use this skill when building community programs, moderating forums, creating advocacy programs, or managing feedback loops. Triggers on community management, forum moderation, advocacy programs, community engagement, feedback loops, community metrics, and any task requiring community strategy or operations.
Agent skill for safla-neural - invoke with $agent-safla-neural
Apply George Mack's High Agency approach to founder and leadership execution. Use when facing ambiguity, blockers, stalled execution, "impossible" constraints, cross-functional deadlock, or high-uncertainty decisions that require ownership and rapid action.
Self-evolving context protocol that captures insights, prevents repeated mistakes, and evolves project documentation through structured feedback loops.