AI & ML

pramoddutta Bundle

Test LLM, RAG, MCP, and agentic systems end to end. Build golden datasets, run deterministic checks and LLM judges, score retrieval, probe prompt injection, verify tool use, and gate CI on thresholds. Orchestrates DeepEval, Ragas, promptfoo, and Langfuse.

pramoddutta Skill

Instrument LLM apps with Langfuse tracing, then use traces, scores, and datasets to test in production, run evaluations on real traffic, catch regressions, and close the loop from incident to golden dataset.

tonone-ai Skill

ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".

tonone-ai Skill

Design an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".

outlinedriven-odin-claude-plugin Bundle

Use when pinning down domain terminology, maintaining the domain model, or when a term conflicts or needs sharpening.

tryboy869 Bundle

[Applies to: **/*.py] Enforce modern, maintainable, and performant PyQt application development standards by leveraging Qt Designer, Model-View architecture, and responsive threading.

tryboy869 Bundle

[Applies to: **/*.py] Definitive guidelines for writing high-performance, maintainable, and production-ready LLM inference code using vLLM.

pramoddutta Skill

Grade LLM and agent traces with OpenAI Evals - build datasets, configure string/python/model graders, run eval suites, and gate agent behavior changes in CI.

tonone-ai Skill

Run human-to-agent migration - audit which roles can be agent-assisted or replaced, design the transition playbook, and manage the offboarding of displaced roles. Use when asked to "which roles can agents replace", "how do we transition to AI-first ops", or "build our agent migration plan".

tonone-ai Skill

Design guardrail layers — input classifiers, output validators, PII scrubbers, policy rule engines. Use when asked to "add safety filters", "scrub PII from LLM output", or "design content moderation".

tonone-ai Skill

Customer segmentation model builder — tiers customers by ARR, health, and expansion potential; defines CS motion per tier; maps resource allocation. Use when asked to "segment our customers", "define our CS tiers", "how should we allocate CS resources", "build a customer segmentation model", or "who gets high-touch vs. digital".

outlinedriven-odin-claude-plugin Bundle

Use when authoring or restructuring an agent-consumed document, a SKILL.md or skill directory, or deciding a skill split-or-monolith disclosure question. Not for prose style: use unslop.

tonone-ai Skill

Build a service catalog — schema, starter entries, and governance model. Produces what information to capture per service, how it's maintained, and where it lives. Use when asked to "service catalog", "what services do we have", "catalog our services", "service inventory", or "who owns what".

tonone-ai Skill

Audit prompt library — duplication, quality, coverage gaps, version drift, eval alignment. Use when asked to "audit our prompt library", "find duplicate prompts", or "check for prompt version drift".

pramoddutta Skill

Teaches the agent to handle popups, new tabs, and multiple browser windows in Playwright using waitForEvent('page'), context pages, and reliable tab switching for OAuth and target=_blank links.

pramoddutta Skill

Teach agents to use the Chrome DevTools MCP server for performance testing with traces, Core Web Vitals, throttling, and evidence-based analysis.

outlinedriven-odin-claude-plugin Bundle

Use when writing a Linux i2c_driver or spi_driver, doing bus register access, DMA-safe SPI transfers, or debugging -EREMOTEIO. Not for MMIO platform drivers: use platform-device-model.

outlinedriven-odin-claude-plugin Bundle

Use when the user requests a userspace C or C++ security review with a threat model and severity filter and wants validated findings. Not for kernel or bare-metal code: use kernel-security.

outlinedriven-odin-claude-plugin Bundle

Use when asked to audit or repair agent surfaces (plugins, agents, skills, CLAUDE.md/AGENTS.md, docs, prompts, commands, hooks) or improve one skill at depth. Not for agent grading: use skill-doctor.

tonone-ai Skill

Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".

tonone-ai Skill

Build a forecasting model for a time series — demand, revenue, or usage prediction. Use when asked to "forecast demand", "predict next quarter revenue", or "build a time series model".

tonone-ai Skill

Validate and benchmark a forecasting model — walk-forward CV, error metrics, baseline comparison. Use when asked "is this forecast any good", "validate a forecasting model", or "backtest the forecast".

tonone-ai Skill

Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".

outlinedriven-odin-claude-plugin Bundle

Use when writing, optimizing, or benchmarking a C++ CPU kernel with AVX2 or AVX512 intrinsics for the Hugging Face kernels ecosystem. Not for CUDA kernels: use cuda.

outlinedriven-odin-claude-plugin Bundle

Use when understanding the Rust Future poll model, Pin and Unpin, tokio scheduling, async stack traces, waker leaks, or select!/join! behavior.

tonone-ai Skill

Design a drift monitoring system for a production ML model. Use when asked to "monitor this model in production", "detect data drift", or "set up ML monitoring".

tonone-ai Skill

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

tonone-ai Skill

Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".

tonone-ai Skill

Build automated regression suites — golden sets, threshold alerting, CI integration for model changes. Use when asked to "catch model regressions in CI", "build a golden set", or "automate eval regression".

tryboy869 Bundle

[Applies to: **/*.py] Definitive guidelines for building robust, maintainable, and scalable multi-agent systems using CrewAI, focusing on modularity, clear role definition, and disciplined LLM configuration.

tryboy869 Bundle

[Applies to: **/*.py] This guide outlines definitive best practices for using the gensim library, focusing on reproducibility, efficient corpus construction, robust model training, and maintainable code for NLP topic modeling tasks.

outlinedriven-odin-claude-plugin Bundle

Use when writing a Linux char driver: file_operations, cdev, copy_to_user, ioctl commands, device memory mmap, or poll. Not for probe and remove lifecycle: use platform-device-model.

tonone-ai Skill

Design production prompts — system prompt architecture, instruction clarity, few-shot selection. Use when asked to "write a system prompt", "design a production prompt", or "pick few-shot examples for this task".

tonone-ai Skill

Compare two or more models statistically — significance testing and error analysis. Use when asked "which model is better", "is this improvement significant", or "compare model performance".

tonone-ai Skill

Produce a threat model — assets, ranked threats, mitigations, accepted risks. Use when asked to "threat model this", "what could go wrong security-wise", "map our attack surface", or before designing any security-sensitive feature.

outlinedriven-odin-claude-plugin Bundle

Use when asked to review learning progress after a milestone. Not for engineering retrospectives, use engineering-retrospective. For agent-environment ones, use agent-environment-retrospective.