AI & ML

pjt222 Skill

Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

pjt222 Skill

Install skills, agents, and teams from agent-almanac into any supported agentic framework using the CLI. Covers framework detection, content search, installation with dependency resolution, health auditing, and manifest-based syncing. Use when setting up a new project with agentic capabilities, installing specific skills or entire domains, targeting multiple frameworks simultaneously, or maintaining a declarative manifest of installed content.

pjt222 Skill

分析任意代码库以识别适合作为 MCP 工具暴露的函数、API 和数据源，生成工具规格文档。 适用于为现有项目规划 MCP 服务器、在将代码库包装为 AI 可访问工具界面前进行审计、 比较代码库的功能与已通过 MCP 暴露的内容，或生成工具规格以交接给 scaffold-mcp-server。

pjt222 Skill

重启后一致的代理行为——渐进式身份加载、从持久化制品重建工作上下文、新启动与继续 检测、通过居中和调谐进行校准，以及身份连贯性验证。解决代理必须从证据而非记忆 重建自身身份和工作内容的冷启动问题。适用于每次新会话开始时、会话中断或崩溃后、 代理行为与先前会话不一致时，或持久化记忆与当前上下文出现矛盾时。

pjt222 Skill

通过评估社区健康、被取代风险、架构对齐和治理可持续性，评估开源代理框架 的投资就绪度。产出四档分类（INVEST / EVALUATE-FURTHER / CONTRIBUTE-CAUTIOUSLY / AVOID），在投入工程精力前指导资源分配决策。

pjt222 Skill

既存の成果物（憲章、バックログ、スプリント計画、WBS）を読み取り、 メトリクスを計算し、ブロッカーを特定し、スケジュール・スコープ・予算・品質の RAGインジケーターを使って進捗を要約することでプロジェクトステータスレポートを 生成します。スプリントまたは報告期間の終了時、ステークホルダーが健全性の 更新を求める場合、ステアリングコミッティまたはガバナンス会議の前、 または新しいブロッカーやリスクがプロジェクト途中で発生した時に使用します。

pjt222 Skill

Create a new SKILL.md file following the Agent Skills open standard (agentskills.io). Covers frontmatter schema, section structure, writing effective procedures with Expected/On failure pairs, validation checklists, cross-referencing, and registry integration. Use when codifying a repeatable procedure for agents, adding a new capability to the skills library, converting a guide or runbook into agent-consumable format, or standardizing a workflow across projects or teams.

pjt222 Skill

Evolve an existing agent definition by refining its persona in-place or creating an advanced variant. Covers assessing the current agent against best practices, gathering evolution requirements, choosing scope (refinement vs. variant), applying changes to skills, tools, capabilities, and limitations, updating version metadata, and synchronizing the registry and cross-references. Use when an agent's skills list is outdated, user feedback reveals capability gaps, tool requirements have changed, an advanced variant is needed alongside the original, or the agent's scope needs sharpening after real-world use.

pjt222 Skill

Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

pjt222 Skill

審、分、擇之忘儲憶。含憶之列與依型/齡/取頻分、識陳訊之朽、以外錨行真實察、 選刪之決樹、為避再生而於敗策施反憶接種、防未來憶污染之先濾規、忘本身可審之留跡。 憶長大未理、項狀自憶書時已大變、取質衰、或為與 manage-memory 並之定期維時用之。

pjt222 Skill

Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.

pjt222 Skill

Launch all available agents in parallel waves for open-ended hypothesis generation on problems where the correct domain is unknown. Use when facing a cross-domain problem with no clear starting point, when single-agent approaches have stalled, or when diverse perspectives are more valuable than deep expertise. Produces a ranked hypothesis set with convergence analysis and adversarial refinement.

pjt222 Skill

为 R 项目创建基于 rocker 基础镜像的 Dockerfile。涵盖系统依赖安装、 R 包安装、renv 集成以及优化的层排序以实现快速重建。适用于容器化 R 应用程序或分析、创建可重现的 R 环境、部署基于 R 的服务（Shiny、 Plumber、MCP 服务器），或在多台机器间建立一致的开发环境。

pjt222 Skill

Set up systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Use when starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.

pjt222 Skill

Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.

pjt222 Skill

Design monochrome ornamental patterns grounded in Alexander Speltz's classical ornament taxonomy. Covers historical period selection, motif structural analysis, prompt construction for line art and silhouette rendering, and AI-assisted image generation via Z-Image. Use when creating decorative borders, medallions, or friezes in a single color, exploring historical ornament styles through generative AI, producing line art or pen-and-ink renderings of classical motifs, or generating reference imagery for design or educational materials.

pjt222 Skill

Export and optimize 3D models for FDM/SLA printing including STL/3MF export, mesh integrity verification, wall thickness checking, support generation, and slicing. Use when exporting from CAD or modeling software for 3D printing, verifying STL/3MF files are printable before slicing, troubleshooting models that fail to slice correctly, optimizing part orientation for strength or surface finish, or converting between model formats while preserving printability.

pjt222 Skill

Achieve distributed agreement without central authority using bee democracy, threshold voting, and quorum sensing. Covers proposal generation, advocacy dynamics, commitment thresholds, deadlock resolution, and consensus quality assessment. Use when a group must decide between options without a designated leader, when centralized decision-making is a bottleneck, when stakeholders have different perspectives to integrate, or when designing automated systems that must reach consensus such as distributed databases or multi-agent AI.

pjt222 Skill

Triage maintenance problems by severity, document findings with context, route to appropriate specialist agent or human, and create actionable issue reports. Use when a maintenance task encounters problems beyond automated cleanup: code that is unsafe to delete, configuration changes requiring domain expertise, breaking changes detected during cleanup, complex refactoring needed, or security-sensitive findings such as hardcoded secrets or vulnerabilities.

pjt222 Skill

Create an effective CLAUDE.md file that provides project-specific instructions to AI coding assistants. Covers structure, common sections, do/don't patterns, and integration with MCP servers and agent definitions. Use when starting a new project where AI assistants will be used, improving AI behavior on an existing project, documenting project conventions and constraints, or integrating MCP servers or agent definitions into a project workflow.

pjt222 Skill

Design an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.

pjt222 Skill

通过读取现有项目文档（章程、待办事项列表、冲刺计划、WBS）， 计算指标、识别阻碍项，并使用 RAG 指示器汇总进度，生成项目状态报告， 涵盖进度、范围、预算和质量维度。适合在冲刺或报告周期结束时使用， 当干系人请求健康状态更新、在指导委员会或治理会议前， 或当新的阻碍项或风险在项目中期出现时使用。

pjt222 Skill

Apply collective intelligence coordination patterns — stigmergy, local rules, and quorum sensing — to organize distributed systems, teams, or workflows without centralized control. Covers signal design, agent autonomy boundaries, emergent behavior cultivation, and feedback loop tuning. Use when designing distributed systems without a coordination bottleneck, organizing teams that must self-coordinate, building event-driven architectures with shared state communication, or replacing fragile centralized orchestration with resilient emergent coordination.

pjt222 Skill

Achieve distributed agreement without central authority using bee democracy, threshold voting, and quorum sensing. Covers proposal generation, advocacy dynamics, commitment thresholds, deadlock resolution, and consensus quality assessment. Use when a group must decide between options without a designated leader, when centralized decision-making is a bottleneck, when stakeholders have different perspectives to integrate, or when designing automated systems that must reach consensus such as distributed databases or multi-agent AI.

pjt222 Skill

Triage maintenance problems by severity, document findings with context, route to appropriate specialist agent or human, and create actionable issue reports. Use when a maintenance task encounters problems beyond automated cleanup: code that is unsafe to delete, configuration changes requiring domain expertise, breaking changes detected during cleanup, complex refactoring needed, or security-sensitive findings such as hardcoded secrets or vulnerabilities.

pjt222 Skill

Create an effective CLAUDE.md file that provides project-specific instructions to AI coding assistants. Covers structure, common sections, do/don't patterns, and integration with MCP servers and agent definitions. Use when starting a new project where AI assistants will be used, improving AI behavior on an existing project, documenting project conventions and constraints, or integrating MCP servers or agent definitions into a project workflow.

pjt222 Skill

Design an A2A Agent Card (.well-known/agent.json) manifest describing agent capabilities, skills, authentication requirements, and supported content types. Use when building an agent that must be discoverable by other A2A-compliant agents, exposing capabilities for multi-agent orchestration, migrating an existing agent to the A2A protocol, defining the public contract for an agent before implementation, or integrating with agent registries that consume Agent Cards.

pjt222 Skill

Analyze the dynamics of diffusion processes using stochastic differential equations, Fokker-Planck equations, first-passage time distributions, and parameter sensitivity analysis. Use when deriving probability density evolution for a continuous-time diffusion process, computing mean first-passage times for bounded diffusion, analyzing how drift and diffusion parameters affect process behavior, or validating closed-form solutions against stochastic simulation.

pjt222 Skill

確率微分方程式、フォッカー・プランク方程式、初通過時間分布、パラメータ感度分析を 使用して拡散過程のダイナミクスを分析する。連続時間拡散過程の確率密度の時間発展を 導出する時、有界拡散の平均初通過時間を計算する時、ドリフトと拡散パラメータが プロセスの挙動にどう影響するかを分析する時、閉形式解を確率シミュレーションに対して 検証する時に使用する。

pjt222 Skill

Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

pjt222 Skill

Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective deletion, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.

pjt222 Skill

Design and execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment. Measure performance differences and make data-driven rollout decisions. Use to validate a new model before full rollout, compare candidate models from different algorithms, measure business metric impact of model changes, or meet regulatory gradual rollout requirements.

pjt222 Skill

Launch all available agents in parallel waves for open-ended hypothesis generation on problems where the correct domain is unknown. Use when facing a cross-domain problem with no clear starting point, when single-agent approaches have stalled, or when diverse perspectives are more valuable than deep expertise. Produces a ranked hypothesis set with convergence analysis and adversarial refinement.

pjt222 Skill

Register trained models in MLflow Model Registry w/ ver control, stage transitions (Staging, Production, Archived) w/ approval workflows, manage lineage w/ metadata + deployment tracking. Use → promote model exp→prod, manage multi vers across stages, approval workflow governance, rollback, audit compliance.

pjt222 Skill

Translate agent-almanac content (skills, agents, teams, guides) → target locale, preserve code blocks, IDs, tech structure. Scaffolding, frontmatter setup, prose translation, code preservation, freshness tracking. Use → localize for new lang, update stale translations after src changes, batch-translate domain.

pjt222 Skill

Build a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.