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Found 185 Skills
Produces a one-page cross-functional business snapshot for SMB owners — cash position (QuickBooks), sales trend (PayPal/Square), pipeline movement (HubSpot), this week's commitments (Calendar), urgent watch-list items (Gmail/Slack), and the single most important thing needing attention today. Proactively tries every available connector and gracefully scopes to whatever is connected — one connector gives a partial pulse; the full stack gives the full picture. Trigger when the user asks how the business is doing, wants a snapshot, a weekly summary, a Monday brief, or says anything like "what am I missing" or "catch me up on the business."
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
Self-report agent issues by logging user corrections for later review, then resume with the correct skill. Use when a user says "don’t do that", "stop doing X", "always do Y", or requests self-correction.
When the user wants to analyze competitors' App Store strategy, find keyword gaps, or understand competitive positioning. Also use when the user mentions "competitor analysis", "competitive research", "keyword gap", "what are my competitors doing", or "compare my app to". For keyword-specific research, see keyword-research. For metadata writing, see metadata-optimization.
Expert product strategy and product marketing skill. Use when defining product vision, assessing product-market fit, sizing market opportunities, doing competitive positioning, building growth loops, designing PLG strategies, creating product marketing context, or using the Working Backwards methodology. Activates for: product strategy, product marketing, PMM, product manager, product management, growth product manager, working backwards, PR/FAQ, Amazon PR FAQ, product roadmap, product positioning, product-market fit, product launch, feature prioritization, TAM SAM SOM, market sizing, competitive moat, business model design, monetization strategy, north star metric, activation, retention, growth loops, freemium, PLG, product-led growth, growth experimentation, ICP context, marketing context document.
Expert methodology for analyzing and summarizing research papers, extracting key contributions, methodological details, and contextualizing findings. Use when reading papers from PDFs, DOIs, or URLs to create structured summaries for researchers.
Boîte à outils complète pour la manipulation de PDF : extraction de texte et tableaux, création de nouveaux PDF, fusion/découpage de documents et gestion de formulaires. Quand Claude doit remplir un formulaire PDF ou traiter, générer ou analyser des documents PDF de manière programmatique et à grande échelle.
Test quality review drawing on twelve classic engineering books — with primary focus on xUnit Test Patterns, The Art of Unit Testing, How Google Tests Software, and Working Effectively with Legacy Code — that diagnoses structural problems in an existing test suite: brittleness, mock abuse, coverage illusions, slow execution, poor readability. Triggers when: user asks about test quality, shares test files for review, or expresses frustration: "tests keep breaking whenever I change anything", "our tests take forever", "I can't understand what this test is doing", "tests pass but bugs still reach production", "we have too many mocks". Do NOT trigger for: writing new tests from scratch (use the regular test-writing workflow) or testing framework/syntax questions — this skill reviews an existing suite for structural quality problems, not individual test authoring.
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Search Web of Science by topic, author, title, DOI, or advanced query. Supports edition/database filtering and sort.
Interact with your Zotero library to sync references, add citations by DOI/ISBN/PMID, and manage PDFs. Triggers on Zotero-related requests.