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Found 875 Skills
Analyze year-over-year growth in income statement items and financial metrics using Octagon MCP. Use when retrieving YoY Revenue Growth, Cost of Revenue Growth, Gross Profit Growth, Operating Income Growth, Net Income Growth, or comparing financial performance across fiscal periods for any public company.
Analyze mindshare, sentiment, and broader social metrics for a particular entity using Kaito MCP tools. Use this skill when the user asks about the social pulse of a particular entity, wants mindshare or sentiment trends, or wants a deeper anomaly-based explanation.
Update Margin Dashboard with Fidelity balance data and calculate margin-living strategy metrics. Monitors margin balance, interest costs, coverage ratios, and scaling thresholds. Triggers safety alerts for large draws and provides time-based scaling recommendations. Use when updating margin, balances, coverage ratio, or margin strategy analysis.
Core reference for DefiLlama MCP tools. Maps DeFi questions to the correct tool call with proper parameters. Covers entity conventions, metric interpretation, stock vs flow distinctions, percentage formatting, and error recovery. Use whenever querying DeFi data — protocol TVL, token prices, chain metrics, fees, revenue, yields, stablecoins, bridges, ETFs, hacks, raises, treasuries, or institutional holdings.
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Debug, develop, and operate apps hosted on Railway (railway.com) from the CLI — list projects/services, tail and filter build/deploy/HTTP logs, read metrics, inspect and set variables, deploy from the current directory, redeploy / restart / roll back, run local commands with the service's env, SSH into containers, and open a DB shell. Authenticates via the `RAILWAY_TOKEN` environment variable (account token, or project-scoped token). Optional bundled scripts (`scripts/preflight.sh`, `scripts/debug.sh`, `scripts/smoke.sh`) are Onsager-specific wrappers — other repos can ignore them or fork. Triggers include "deploy to railway", "railway deploy this", "railway logs", "tail railway logs", "why is my railway service crashing", "why did the build fail on railway", "railway 500s", "railway latency", "show railway http logs", "redeploy on railway", "restart my railway service", "roll back railway", "set a railway env var", "list railway variables", "railway metrics", "is my railway service healthy", "connect to my railway postgres", "ssh into railway", "run this locally with railway env", "list railway projects/services/deployments", and (Onsager-specific) "check railway", "preflight", "smoke test", "is the deploy healthy".
Guides organizational and business storytelling—narrative structure (setup, tension, resolution), audience-tailored stories for executives, customers, boards, and teams, honest data and metrics framing, product and strategy narratives, incident and postmortem storytelling, and actuarial or insurance risk narratives for non-technical audiences. Covers story spine, key messages, and visual or slide narrative outlines. Use when the user says "tell the story", "storytelling", "narrative for executives", "data story", "board presentation narrative", "explain with a story", "story arc", "key message", "compelling narrative", "pitch story", or "incident story"—not cross-department reframing only (cross-department-translation), company-wide comms cadence and crisis wording packs (communication-lead), long-form creative fiction or screenwriting, brand copy without strategy context, or technical documentation and API reference (tech-writer-researcher).
Migrate configuration from Bluejay voice AI testing platform to Coval. Use when customer says "migrate from bluejay", "bluejay migration", "import bluejay config", or needs to transfer agents, simulations, metrics, and schedules from Bluejay to Coval.
Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics