Analiza datos de comentarios de usuarios para identificar segmentos con puntuaciones de sentimiento, trabajos por hacer (JTBD) e información sobre satisfacción del producto. Útil al analizar comentarios de usuarios a gran escala, realizar análisis de sentimiento en reseñas o encuestas, o identificar patrones de satisfacción.
Esta habilidad permite a un agente de IA procesar grandes volúmenes de comentarios de usuarios, como reseñas o encuestas, para extraer segmentos con puntuaciones de sentimiento, identificar los trabajos por hacer (JTBD, por sus siglas en inglés) y descubrir información detallada sobre la satisfacción del producto. Es especialmente útil para equipos de producto, atención al cliente o investigación de usuarios que necesitan comprender rápidamente las tendencias emocionales y las necesidades no satisfechas en los comentarios sin leer cada uno individualmente. La habilidad se basa en procesamiento de lenguaje natural (NLP) y modelos de análisis de sentimiento para clasificar comentarios como positivos, negativos o neutrales, y luego agruparlos en segmentos significativos. Además, puede utilizar la metodología JTBD para mapear los comentarios a trabajos funcionales, sociales y emocionales que los usuarios intentan realizar, proporcionando así una visión más profunda de las motivaciones subyacentes. Al identificar patrones de satisfacción en diferentes segmentos, los equipos pueden priorizar mejoras, detectar problemas recurrentes y ajustar la estrategia del producto. Esta habilidad es compatible con agentes como Claude Code, Cursor y Codex, y se integra fácilmente en flujos de trabajo de análisis de datos.
npx skills add https://github.com/phuryn/pm-skills/tree/main/skills/sentiment-analysisInstalled? Explore more Investigación y análisis de datos skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Analiza datos de comentarios de usuarios para identificar segmentos con puntuaciones de sentimiento, trabajos por hacer (JTBD) e información sobre satisfacción del producto. Útil al analizar comentarios de usuarios a gran escala, realizar análisis de sentimiento en reseñas o encuestas, o identificar patrones de satisfacción.
This repo contains 20 individual skills — each has its own dedicated page.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Analyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
Generate an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
Brainstorm product ideas for an existing product using multi-perspective ideation from PM, Designer, and Engineer viewpoints. Use when generating new feature ideas, brainstorming solutions for an identified opportunity, or ideating with a product trio.
Brainstorm feature ideas for a new product in initial discovery from PM, Designer, and Engineer perspectives. Use when starting product discovery for a new product, exploring features for a startup idea, or doing initial ideation.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Generate a Business Model Canvas with all 9 building blocks. Use when creating a business model, documenting how a business creates value, or analyzing an existing business model.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Create sales-ready competitive battlecards comparing your product against a specific competitor — positioning, feature comparison, objection handling, and win/loss patterns. Use when preparing sales teams, creating competitive materials, or responding to 'why not competitor X?'
Analyze competitors with strengths, weaknesses, and differentiation opportunities. Identifies direct competitors and maps the competitive landscape. Use when doing competitive research, preparing a competitive brief, or finding differentiation opportunities.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Create an end-to-end customer journey map with stages, touchpoints, emotions, pain points, and opportunities. Use when mapping the customer experience, identifying friction points, improving onboarding, or visualizing the user journey.
Draft a detailed Non-Disclosure Agreement between two parties covering information types, jurisdiction, and clauses needing legal review. Use when creating confidentiality agreements or preparing an NDA for a partnership.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
Identify grammar, logical, and flow errors in text and suggest targeted fixes without rewriting the entire text. Use when proofreading content, checking writing quality, or reviewing a draft.
Identify growth loops (flywheels) for sustainable traction. Evaluates 5 loop types: Viral, Usage, Collaboration, User-Generated, and Referral. Use when designing growth mechanisms, building product-led traction, or understanding how growth loops work.
Identify the best GTM motions and tools across 7 motion types: Inbound, Outbound, Paid Digital, Community, Partners, ABM, and PLG. Use when selecting marketing channels, choosing between inbound and outbound strategy, or planning cross-channel campaigns.
Create a go-to-market strategy covering marketing channels, messaging, success metrics, and launch timeline. Use when planning a product launch, creating a GTM plan from scratch, or defining a launch strategy for a new market.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.