Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.
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borghei/Claude-Skills☆ 948Updated Oct 7, 2026
Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.
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GarethManning/education-agent-skills☆ 880Updated Aug 29, 2026
Design a wise systems intervention from an existing analysis. Maps proposed actions against Meadows' leverage points, checks for unintended consequences, and generates alternatives.
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nota-america/forgecat-agent-profiles☆ 91Updated Sep 24, 2026
Feedback loop implementation for continuous CTI improvement. Consumer feedback, analyst retrospectives, source quality tracking.
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Liberty91LTD/cti-skills☆ 27Updated Sep 30, 2026
Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.
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ShulkwiSEC/bb-huge☆ 23Updated Jul 11, 2026
Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.
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curiositech/windags-skills☆ 16Updated Oct 14, 2026
Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.
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curiositech/port-daddy☆ 3Updated Oct 18, 2026
Diagnose when intuitive judgment, agent confidence, or expert routing can be trusted by classifying environment validity, feedback quality, and task-boundary fit. Use for confidence calibration, agent routing, expertise audits, and escalation design. NOT for deterministic implementation tasks, pure syntax debugging, or domains with explicit verifiable answers.
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curiositech/port-daddy☆ 3Updated Oct 18, 2026