Tracks ChatGPT and Codex usage limits, global Tibo reset announcements, silent platform resets, and multi-account quotas in real time.
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Analytics & Monitoring
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on…
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Instrument a webapp to send useful telemetry data to Azure App Insights
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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods.
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Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward…
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Compress large language models using knowledge distillation from teacher to student models.
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Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression…
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Provides thread-aware debugging and health monitoring for FreeRTOS, RT-Thread, and Zephyr embedded systems.
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Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, and observability…
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Generate a "Journey Into [Project]" narrative report analyzing a project's entire development history from claude-mem's timeline.
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Use when the user wants to list or search AI Observability conversations, inspect generations, manage evaluators (create, test, delete)…
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Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights.
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Access raw conversation history from Claude Code session storage for analysis
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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step…
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Automated TAM/SAM/SOM calculations with methodology documentation
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Health check for TTS and Telegram bot subsystems.
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Xactly incentive compensation management for sales performance
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Deep integration with product analytics platforms for metrics, funnels, retention, and experimentation.
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Generates and renders interactive data visualizations across 20+ chart types using AI-friendly GPT-Vis syntax for modern web frameworks.
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AI-powered API and webhook monitoring skill for e-commerce integrations.
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Map AI cost topology — billing attribution, team-level spend, forecast vs actuals, alert gaps.
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Analytics & Monitoring agent skills on mcprush hold a number to its definition: what a metric counts, what rows it drops, and what an experiment is allowed to conclude from the interval it actually produced. The kinds are workflow, expertise, output format, voice & style and guardrail, and the one a skill belongs to, where its author declared it, is the fastest way to guess what it will do to your prompt.
What to look at before installing one is what it costs in context and what it takes away: a guardrail that refuses an action and a voice that rewrites a sentence are both skills, and only one of them will stop the agent doing something. Every skill here says what it costs in context, which kind it is where its author declared one, what it needs beside it to work and the licence it is published under, next to the publisher it came from and whether that publisher has been verified. The filters on the left narrow it further and the sort at the top decides what comes first.
A skill is not connected to anything: there is no endpoint to authorise and nothing routed through the gateway. Installing one drops a folder of instructions into the client’s own skill folder, and uninstalling it is deleting that folder. The exact line for each client is on the skill’s own page, because it differs by client rather than by skill.