when-optimizing-agent-learning-use-reasoningbank-intelligence
Implement adaptive agent learning with ReasoningBank
Agent performance plateaus without learning from experience. ReasoningBank captures decision trajectories, extracts patterns, and trains models to continuously improve agent strategies over time.
تنزيل ZIP المهارة
رفع في Claude
اذهب إلى Settings → Capabilities → Skills → Upload skill
فعّل وابدأ الاستخدام
اختبرها
استخدام "when-optimizing-agent-learning-use-reasoningbank-intelligence". Initialize ReasoningBank and capture 20 agent trajectories
النتيجة المتوقعة:
- Learning system initialized with 20 trajectories captured
- Pattern extraction: 5 clusters identified with 85 percent similarity threshold
- Top pattern: error recovery sequence with 92 percent success rate
- Decision model trained: 100 epochs, 32 batch size
- Performance improvement: 23 percent faster task completion
- Integration guide generated and model exported
استخدام "when-optimizing-agent-learning-use-reasoningbank-intelligence". Train decision model on patterns and benchmark results
النتيجة المتوقعة:
- Decision Transformer model created with 256 hidden size
- Training completed with 0.002 loss after 100 epochs
- Baseline agent average score: 72 percent
- Optimized agent average score: 89 percent
- Performance improvement: 23.6 percent
- Model exported to /tmp/reasoningbank-export.json
التدقيق الأمني
آمنPure documentation skill containing markdown files only (SKILL.md, PROCESS.md, README.md). No executable code files exist (.js, .py files). All 88 static findings are false positives caused by the analyzer incorrectly flagging markdown code examples as actual command execution. The skill is instructional content for ML libraries with no network calls, no credential handling, and no file system operations beyond documentation examples.
عوامل الخطر
⚡ يحتوي على سكربتات
🌐 الوصول إلى الشبكة
📁 الوصول إلى نظام الملفات
⚙️ الأوامر الخارجية
درجة الجودة
ماذا يمكنك بناءه
Build self-improving agents
Create agents that learn from experience and optimize their decision-making over time
Experiment with RL algorithms
Test and compare 9 reinforcement learning algorithms for agent strategy optimization
Optimize repetitive workflows
Automatically identify and apply patterns from successful task executions
جرّب هذه الموجهات
Initialize ReasoningBank with trajectory tracking, register schema, and configure verdict criteria for my agent
Capture agent decision trajectories and extract patterns using vector similarity with 0.85 threshold
Train a Decision Transformer model on extracted patterns and generate top 5 strategy recommendations
Benchmark baseline versus optimized agent performance and export the trained model for production deployment
أفضل الممارسات
- Collect diverse trajectories including both successful and failed attempts for balanced learning
- Validate patterns with at least 80 percent success rate before applying optimizations
- Monitor production performance after deployment and retrain models regularly
تجنب
- Applying optimizations without validating pattern success rates first
- Training on insufficient trajectory data with fewer than 10 samples
- Skipping the benchmark comparison between baseline and optimized agents