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概要と使いどころ
Interactive assistant for intercepting, debugging, analyzing and reviewing Claude Code API requests using mitmproxy. Guides setup, certificate configuration, and active traffic inspection via web or CLI interface. Supports learning Claude Code internals, debugging issues, and optimizing API usage.
日本語の概要は準備中です。原文の説明を表示しています。
' Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems'
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
日本語の概要は準備中です。原文の説明を表示しています。
Illustrative Claude Code skill configuration. Use when learning the basic SKILL.md shape; follow the canonical runtime reference for the current field inventory.
日本語の概要は準備中です。原文の説明を表示しています。
Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.
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Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.
日本語の概要は準備中です。原文の説明を表示しています。
Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.
日本語の概要は準備中です。原文の説明を表示しています。
Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.
日本語の概要は準備中です。原文の説明を表示しています。
Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.
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Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation analysis, statistical methods (ADF/GARCH), strategy optimization, execution modeling, hedging, and ML-based prediction (sklearn). Also provides CLI access to run indicator scripts against K-line data. Triggers: "量化", "因子", "配对交易", "协整", "波动率策略", "季节性", "多因子", "IC", "机器学习", "对冲", "量化策略", "協整", "波動率策略", "季節性", "多因子", "對沖", "quant", "pairs trading", "cointegration", "volatility strategy", "seasonality", "multi-factor", "factor model", "IC IR", "machine learning", "hedging", "walk-forward", "配對交易", "機器學習", "因子選股"
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Записать W-рефлексию (мировоззренческий слот RCS) в learning.w_reflections. Используется Диагностом R28 (MIM.R.009) или вызывается пользователем после рефлексивной сессии. Влияет на gate Д→П (FORM.089 §5.2).
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Диагностика ступени мастерства (Диагност R28, FORM.089 §6.1 v5.0) прямо в VS Code / claude.ai. До 6 вопросов, ~3 мин. Сохраняет cp-профиль в канонический журнал learning.cp_assessments — через MCP-инструмент (browser) или напрямую в Neon (VS Code). Запускай когда: пилот говорит «пройди диагностику», «какая моя ступень», «/diagnose» — или ПРОАКТИВНО когда видишь пустой cp_profile или нового пользователя без данных.
日本語の概要は準備中です。原文の説明を表示しています。
Управление consent в learning.tracking_consent — opt-in / opt-out / status / revoke. Обязательное условие для работы stage_evaluator worker (WP-253 Блок 2). GDPR-совместимо.
日本語の概要は準備中です。原文の説明を表示しています。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.
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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.
日本語の概要は準備中です。原文の説明を表示しています。
Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems
日本語の概要は準備中です。原文の説明を表示しています。
Expert knowledge synthesizer specializing in extracting insights from multi-agent interactions, identifying patterns, and building collective intelligence. Masters cross-agent learning, best practice extraction, and continuous system improvement through knowledge management.
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Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.
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Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.
日本語の概要は準備中です。原文の説明を表示しています。