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Found 1,660 Skills
Audit your biggest closed-won deals to find your PROVEN ideal customer profile, then find more accounts like them. Use whenever someone wants to analyze won deals, audit their best customers, see which companies generated the most revenue, find their real ICP, build a look-alike target list, segment customers by what actually pays, or learn which acquisition channel produced their best revenue. Triggers on: 'audit my biggest deals', 'which customers made us the most money', 'analyze my closed-won', 'what's my proven ICP', 'find more customers like my best ones', 'look-alike accounts', 'HubSpot deal analysis', 'revenue by account', 'which channel generated my best deals', 'acquisition source analysis'. For RevOps, Heads of Sales/Marketing, founders and growth leads doing ICP refinement, account-based targeting or pipeline/QBR review. Reads HubSpot via its MCP or a CSV export, then hands the profile to sales-nav-search-builder to generate the prospecting search. Maintained by La Growth Machine.
Rank outreach campaigns by real revenue impact — which campaigns actually generated deals, pipeline, or meetings — by cross-referencing the user's La Growth Machine campaign data with their CRM deal data (HubSpot today). Use whenever the user wants to know which campaigns drove pipeline, compare campaign ROI, see which campaigns to continue / stop / adapt, audit campaign impact, review attribution, asks 'which of my campaigns is actually working', or wants a campaign performance ranking by deals or revenue. Triggers on: 'which campaigns drove pipeline', 'rank my campaigns by deals', 'campaign ROI', 'campaign impact', 'which campaigns to stop', 'which to scale', 'attribution review', 'pipeline by campaign'. Pulls live data from the La Growth Machine MCP and the HubSpot MCP when connected; works from pasted exports otherwise. For RevOps, Heads of Sales/Marketing, founders and growth leads doing campaign performance reviews. Maintained by La Growth Machine.
Anonymize and sanitize customer-provided log files before they are committed as pipeline test fixtures or sample events. Performs a line-by-line review and replaces all sensitive values inline, preserving log structure and format exactly — never reformats, re-indents, or restructures content. Invoke manually with /anonymize-logs.
Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
NestJS best practices and patterns for building scalable, maintainable backend applications. This skill should be used when writing, reviewing, or refactoring NestJS code to ensure proper architecture, security, performance, and code quality. Triggers on tasks involving NestJS modules, controllers, services, guards, pipes, middleware, Prisma database operations, authentication, or any NestJS-specific patterns.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
A documentation-focused skill for game architecture design. Produces technical selection, design, and planning documents through a structured pipeline. Use this skill to generate requirement analysis, technical design, and implementation planning documents for new game projects or major feature development.
Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.