Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 696 files from 2 113 authors, of which 68 413 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
>- Author and maintain MANUAL and hybrid test cases and suites in TestRail, Xray (Jira), Zephyr Scale, and Qase. Covers test-case anatomy (title, preconditions, steps, expected results, test data), suite/section organization, bulk authoring from user stories and acceptance criteria, ambiguous-step linting, CSV/API import-export payloads per tool, requirement traceability and coverage gaps, review hygiene, and when a manual case should graduate to automation. Scale case," "Qase case," "import CSV into TestRail," "lint these steps," "traceability report," "should this be automated." WHAT-to-test selection — that is test-planning.
>- Scheduled probes that run CONTINUOUSLY after release. Covers probe design for critical user journeys, alerting integration, SLA validation, multi-region monitoring," "uptime testing," "scheduled probes," "SLA validation," one-shot post-deploy smoke gate tied to a release — use release-readiness.
>- Design environment strategy for testing across dev, CI, preview, staging, and production — Docker Compose test infrastructure, multi-stage Dockerfiles, seed-data lifecycle, per-PR preview environments, production parity, and external-dependency stubbing at the HTTP boundary. "staging parity," "spin up test infra," "environment tiers." fixture data patterns — use test-data-management; pipeline/Actions config — use ci-cd-integration.
>- Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, "seed data," "synthetic data," "test database," "data anonymization." environment provisioning and database branching strategy — use test-environments.
>- Migrate a test suite from one framework to another, incrementally and without losing coverage. Covers Selenium→Playwright, Cypress→Playwright, Jest→Vitest, Mocha→Vitest, and Protractor→Playwright, with parallel CI running, locator/assertion translation, and a coverage-parity track.
>- Produce a multi-quarter QA strategy document. Covers scope, risk-based prioritization, test levels (unit/integration/E2E), pyramid analysis, entry/exit criteria, quality KPIs, tool selection rationale, CI scaling levers, and timeline "test strategy," "QA strategy doc," "testing approach," "QA roadmap," risk-based-testing first. test-reliability.
>- environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. locator recovery," "unreliable test," "quarantine flaky test." selector-drift-recovery (this skill heals ONE test at runtime; that one bug reports — use ai-bug-triage.
>- Build a single sprint or release test plan. Covers feature decomposition into testable scenarios, requirements-to-test coverage mapping, effort estimation by test type, prioritization matrices (risk × effort), resource allocation, and `release-readiness`.
>- coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery.
>- canary analysis, guardrail metrics, production smoke tests, and synthetic deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe `observability-driven-testing`.
>- Write effective unit tests with Jest, Vitest, or pytest. Covers the test-doubles taxonomy (stub/spy/mock/fake), Arrange-Act-Assert, coverage threshold configuration and CI gating, snapshot testing, fake timers, and mutation testing with Stryker/mutmut. "test doubles," "mutation testing," "fake timers," "snapshot test." AI generating the test code for you — use ai-test-generation; auditing existing tests for smells — use ai-qa-review; browser/component rendering assertions — use cypress-automation or visual-testing.
>- Implement visual regression testing with Playwright screenshots, Chromatic, Percy, and Argos CI. Covers baseline management, diff threshold tuning, dynamic content masking, responsive viewport testing, and review/approval workflows. "update baselines," "Chromatic," "percy snapshot," "argos screenshot." selector-drift-recovery; cross-browser rendering matrices — use cross-browser-testing; general Playwright test structure — use playwright-automation.
Design, implement, review, or troubleshoot safe refinement of editable 2D canvas strokes. Use for handwriting smoothing, automatic text layout cleanup, optional font-guided handwriting normalization, freehand cleanup, geometric shape snapping, diagram cleanup, stroke-style normalization, semantic stroke replacement, and previewable or undoable refinement pipelines in whiteboards, note apps, drawing tools, annotation systems, and pen-input SDKs. Do not use for general raster image editing, prose rewriting, or renderer bugs unrelated to stroke transformation.
| Generate multiple app, favicon, or website icon candidates with either the local QM Icon Studio CLI or Codex built-in image generation with reference icons, then present a contact sheet so the user can choose. Use when building websites or apps and needing reusable icon options.
> 🛒 E-commerce & Retail skill suite derived from alirezarezvani/claude-skills. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.
> 📈 SEO & Content Marketing skill suite derived from ComposioHQ/awesome-claude-skills. Keyword research, content audits, SERP analysis, technical SEO and content strategy. Provides 10 specialised commands for seo, content, marketing workflows.
> 🔒 Security & Compliance skill suite derived from ComposioHQ/awesome-claude-skills. Security audits, vulnerability management, GDPR/SOC2/ISO27001 compliance and incident response. Provides 10 specialised commands for security, compliance, gdpr workflows.
> 🔒 Security & Compliance skill suite derived from travisvn/awesome-claude-skills. Security audits, vulnerability management, GDPR/SOC2/ISO27001 compliance and incident response. Provides 10 specialised commands for security, compliance, gdpr workflows.
> 🤖 Data Science & AI/ML skill suite derived from ComposioHQ/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.
兩模式工具 — Mode A 掃 codebase / SKILL.md / prompt 找重複/命名/模組/過長 4 類技術債；Mode B 跑 repo audit 檢查私公版 sync / release 一致性 / cross-link / 版本標記漂移 + 開源交接健檢 5 維度。觸發詞：「清理 code」「找重複」「重構」「模組化」「review codebase」「掃 prompt」「我這 skill 寫好亂」「prompt 太長拆一下」「跑大專案怕亂」「audit 我的 repo」「check release 一致性」「私公版 diff」「版本對齊」「release ship 前盤點」時觸發。
> IDE-grade project scaffolding wizard for creating new projects with comprehensive configuration. Flutter, Expo, FastAPI, Django, Express, NestJS, Go/Gin, Rust/Axum, Spring Boot, Hono, Elysia, Chrome Extensions, VS Code Extensions, Tauri desktop apps, serverless functions, and more. Provides WebStorm/PyCharm-level project creation with interactive SDK selection, framework a framework application, initializing a codebase, scaffolding boilerplate, building extensions, creating mobile/desktop/web apps, setting up monorepos, or making static websites/landing pages.
> 🛒 E-commerce & Retail skill suite derived from alirezarezvani/claude-code-skill-factory. Product catalogue optimisation, conversion rate, customer journey and retail analytics. Provides 10 specialised commands for ecommerce, retail, shopify workflows.
> ☁️ DevOps & Cloud Infrastructure skill suite derived from alirezarezvani/claude-code-skill-factory. CI/CD pipelines, container orchestration, IaC, monitoring and incident response. Provides 10 specialised commands for devops, cloud, docker workflows.
| Searches the live web via Nimble APIs to monitor competitors and produce a structured intelligence briefing. Runs parallel searches for news, product launches, hiring signals, and funding — then compares against previous findings to highlight only what's new. Use this skill when the user asks about competitors, competitive intelligence, doing", "competitor update", "competitor news", "competitive landscape", "market intel", "what's new with [company]", "track [company]", "competitor briefing", "who's making moves", "competitive analysis", "losing deals to [company]", "battlecard". Also use before board meetings or strategy sessions when the user wants competitive context. Requires the Nimble CLI (nimble search, nimble extract) for live web data. Do NOT use for single-company deep dives (use company-deep-dive), meeting prep with attendees (use meeting-prep), or non-business queries.
| Validates practitioner credentials and license status against the NPI registry. Cross-references specialties, credentials, and practice addresses against official records. Returns Verified / Partially Verified / Unverified / Flagged per practitioner with mismatch details and source URLs. licenses", "verify NPI numbers", "cross-check credentials against NPI", "compliance audit on providers", "are these practitioners still licensed", "validate my provider list". Accepts CSV, Google Sheet URL, or pasted data. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for filling data gaps — use healthcare-providers-enrich instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
| web-data agents, scrapes into Delta tables, and produces an AI/BI dashboard and/or a deployed Databricks App — a table → dashboard → app workflow, for production data products or quick demos. Use whenever a request pairs live or scraped web data WITH a Databricks destination — e.g. "scrape Amazon/Walmart prices into a Delta table and build a dashboard", "load Zillow/Instagram/Maps/search results into Databricks and build a dashboard or app", "showcase Nimble + Databricks to a prospect". Prefer it over nimble-web-expert or competitor-intel when the data lands in Databricks. Do NOT use for one-off web fetches or CSV exports with no Databricks destination — use nimble-web-expert instead. Do NOT use for competitor or company research briefings — use competitor-intel or company-deep-dive instead. Do NOT use for generic Databricks work with no Nimble/web-data angle — use the official databricks-* skills instead.
| Fills gaps in existing healthcare practitioner lists — adds missing phone numbers, credentials, specialties, contact info, education, reviews, and regulatory data. to these doctors", "complete this practitioner database", "enrich CRM export", "fill gaps in my provider data", "supplement this healthcare list". Accepts CSV, Google Sheet URL, or pasted data. Searches for each provider's practice website, extracts missing fields, and enriches with reviews, clinical trials, and accreditation via WSAs. Do NOT use for extracting providers from practice URLs — use healthcare-providers-extract instead. Do NOT use for validating credentials — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
| Extracts structured practitioner data from healthcare practice websites. Returns names, credentials, specialties, contact info, and education for every provider on a practice's site. Use when user asks to extract, pull, or list doctors, providers, or staff from", "who are the providers at", "build a provider database", "list all doctors at", "scrape the team page", "get practitioner data from". Accepts practice URLs (pasted, CSV, Google Sheet) or discovers practices via Google Maps when given specialty + location. Single sites or 100+ URLs. Do NOT use for filling data gaps — use healthcare-providers-enrich instead. Do NOT use for credential validation — use healthcare-providers-verify instead. Do NOT use for discovering practices — use market-finder or local-places instead. Do NOT use for general extraction — use nimble-web-expert instead.
| Finds qualified candidates for a role by searching LinkedIn, Indeed, GitHub, and other professional platforms using Nimble Web Search Agents. Accepts a job description, role title, or freeform request and returns a ranked candidate list with profiles, skills, and contact signals. Use this skill when the user wants to find, source, or recruit candidates for "who can I hire for", "find me a [role]", "recruiting for", "talent search", "find a [role] in [city]", "build a candidate list", "sourcing for [role]", "who's available for", "find potential hires". Also triggers on a pasted job description followed by a sourcing request. Do NOT use for job market research or salary benchmarking — use market-finder instead. Do NOT use for researching a single known person — use company-deep-dive or meeting-prep instead.
| Discovers all businesses of a given type in any geography using Nimble a user's existing list (Google Sheet, CSV, inline) against fresh discovery, categorizing entries as matched, discovered-only, or reference-only. Vertical presets (Healthcare, SaaS, Restaurants, Legal, Auto/Home) auto-select WSA routing. "account universe", "how many X in Y", "TAM for", "discover all", "audit my list", "compare against", "what am I missing", "gap analysis", "verify my business list", "prospect list". Do NOT use for competitor monitoring — use competitor-intel instead. Do NOT use for company deep dives — use company-deep-dive instead. Do NOT use for neighborhood-level exploration with social enrichment — use local-places instead.
| "tell me about [company]", "research [company]", "what does [company] do", "who is [company]", "look up [company]", "company deep dive", "due diligence on [company]", "background on [company]", "dig into [company]", "analyze [company]", or evaluating a company for investment, partnership, or sales. MUST be used instead of answering from memory — fetches real-time web data (funding, leadership changes, product launches, news) your training data lacks. Use even for well-known companies. Produces a sourced 360° report covering funding, leadership, product/tech, market position, news, and strategic outlook with dates and URLs. Do NOT use for multi-company competitor monitoring (use competitor-intel) or meeting prep with attendees (use meeting-prep).
| Scans Reddit, X, LinkedIn, Instagram, TikTok, YouTube, blogs, news, and review platforms for brand mentions — scoring each one across four dimensions (reach, velocity, sentiment, and risk-topic match) so marketing teams can respond before a mention spirals. Source LinkedIn, G2, HN, and trade press; consumer brands get TikTok, Instagram, X, and YouTube. Every mention is bucketed into Crisis / Watch / Engage / Log with a suggested owner and response window. Powered by Nimble. Use when asked to "monitor brand mentions", "scan for brand mentions", "what are people saying about [brand]", "brand monitoring", "social listening", "run a brand sweep", "find high-risk mentions", or any variation of brand monitoring across web and social. Do NOT use for one-time company research or due diligence — use company-deep-dive instead. Do NOT use for competitor messaging/positioning analysis — use competitor-positioning instead. Do NOT use for funding/hiring/business signals on a competitor — use competitor-intel instead.
| Tracks how competitors position themselves online — scrapes homepages, features, pricing, and blogs to extract messaging, value props, CTAs, and pricing models. Compares against previous snapshots to surface positioning shifts with before/after tracking. Produces messaging matrices, content gap analysis, white space maps, and battlecard inputs. Use when anyone asks about competitor messaging, positioning, website copy, positioning", "messaging comparison", "content gap", "what changed on their site", "competitor homepage", "landing page teardown", "marketing battlecard", "how do they describe their product", "share of voice", "counter-messaging". Do NOT use for business signals like funding/hiring (use competitor-intel), single-company deep dives (use company-deep-dive), or meeting prep (use meeting-prep).
| Monitors press, social, developer communities, and competitor channels from any point around a product launch — tracking sentiment, flagging mischaracterizations, surfacing competitor responses, and recommending actions in real time. Nimble reaches sources that block standard agents — including paywalled press, Reddit, LinkedIn, JavaScript-heavy pages, and live community forums — while Claude triages every signal by urgency. Delivers a Response War Room dashboard with a live signal feed, mischaracterization tracker, competitor response panel, and sentiment velocity chart. Use when asked to "monitor this launch", "track the launch", "what's being said about the launch", "flag any mischaracterizations", "competitor response to the launch", "post-launch coverage", "check press coverage for the launch", or "what's the reaction to the announcement". Do NOT use for ongoing brand monitoring unrelated to a launch — use brand-mention-monitor instead. Do NOT use for ongoing competitor intelligence unrelated to a specific launch window — use competitor-intel instead.
| Discovers, enriches, and scores local businesses in any neighborhood using Nimble Web Search Agents (WSAs) and web data. Returns a structured, ranked list with confidence scores, reviews, social presence, and an interactive map. Use this skill when the user asks about local businesses, places, or "map every bar in", "local businesses in", "discover gyms near", "what restaurants are in", "neighborhood guide for", "local places in", "find places near", "list all [business type] in [area]", "best [type] near [location]", "build a neighborhood guide", "local place search". for live web data via WSAs and fallback search. Do NOT use for competitor analysis or monitoring (use competitor-intel), company research or deep dives (use company-deep-dive), general web search or extraction (use nimble-web-expert).
| Researches meeting attendees and their companies before any meeting using real-time web data. Surfaces roles, recent activity, company context, and talking points — then maps cross-attendee relationships. Use this skill when the user asks to prepare for a meeting, research someone for my meeting", "who am I meeting with", "research this person", "meeting prep", "brief me on [person]", "I have a meeting with [person/company]", "get me ready for my call", "what should I know about [person]", "background on [person] before our meeting", "attendee research". Requires the Nimble CLI (nimble search, nimble extract) for live web data. Do NOT use for multi-company competitor monitoring (use competitor-intel) or single-company deep dives without attendees (use company-deep-dive).
| Get web data now — fast, incremental, immediately responsive to what the user needs. The only way Claude can access live websites. Must be pre-installed and authenticated. Run `nimble --version` to verify (>= 1.2.0).
| keyword research, rank tracking, site audits, content gap analysis, competitor keyword reverse-engineering, AI visibility across five platforms (ChatGPT, Perplexity, Google AI, Gemini, Grok), and GitHub repo SEO. Crawls real sites and SERPs via Nimble CLI — no fabricated metrics. "competitor keywords", "AI visibility", "GitHub SEO", "SERP analysis", "keyword research", "technical SEO", "keyword difficulty", "topic clusters", "ranking delta", "on-page SEO", "AI citation audit". Do NOT use for competitor business signals — use `competitor-intel` instead. Do NOT use for competitor messaging — use `competitor-positioning` instead. Do NOT use for general web scraping — use `nimble-web-expert` instead.
| Reference for Nimble Web Search Agents (Agent API V2). Load when a task needs open-ended research, enrichment, or dataset building — where the source isn't fixed, data is scattered, structure is inconsistent, or a synthesized answer is needed. locking, run-level `skill` override, run controls, live events vs polling, trust and citations, and safe credentials.
| Reference for nimble crawl command. Load when bulk-crawling many pages asynchronously.
| Reference for nimble extract command. Load when fetching URLs or scraping pages. parser schemas, geo targeting, async, parallelization.
| Reference for nimble map command. Load when discovering URLs on a site before bulk extraction. response structure {links[].url/title/description}, map→filter→extract pattern, map vs crawl comparison.
| Reference for nimble tasks and batches commands. Load when polling async task status, tracking batch progress, or fetching results. crawl (per-page tasks), search async, map async.
| Reference for nimble search command. Load when searching the live web. search_depth modes (lite/fast/deep), response structure, credit costs.
Create narrated video overviews from source documents using editable storyboard rows, OpenAI text-to-speech audio, OpenAI image generation, captions, and deterministic ffmpeg assembly. Use when Codex needs to turn PDFs, DOCX files, Markdown, text notes, research, meeting materials, briefs, or folders of documents into a NotebookLM-style static-image video overview with voiceover, or when the user wants to edit/regenerate individual narration or image rows without rebuilding the whole project.
Build lightweight mood boards, visual territories, creative directions, style boards, reference boards, art-direction boards, campaign mood explorations, brand look-and-feel explorations, image batches for visual ideation, more-like-this rounds from attached images, and follow-up mood-board iterations from annotated or attached images. Use this standalone skill instead of Creative Production when the user asks Codex to create, generate, explore, iterate, or review mood-board-style image directions.
Record and live-transcribe a meeting or live conversation from a Codex thread on macOS, saving streaming transcript text to the workspace with OpenAI Realtime transcription, then summarize notes in-thread. Use when the user asks to start, stop, capture, transcribe, ask questions about, or summarize a meeting recording from the current thread.
Launch and operate a local Codex drawing workspace: a lightweight vector board where the user and Codex share editable scene state through a browser UI, workspace-backed JSON, local API mutations, and SVG export. Use when the user wants to draw, edit, inspect, generate, refine, or export simple vector artifacts such as logos, icons, diagrams, badges, layout sketches, SVG assets, or visual scratchpad scenes.
Generate NotebookLM-style podcasts from documents or supplied scripts. Use when Codex needs to turn source material into a reviewable single-host, two-host, or multi-host podcast script; create host-marked dialogue with stable IDs; manage client revisions and pronunciation guidance; generate ElevenLabs v3 Text to Dialogue audio in natural chunks; or merge chunked audio with FFmpeg into a final episode.
Create polished executive or client presentation decks in Codex through a staged workflow: optional research, user direction, narrative outline, subagent critique, wireframe, mood boards, and final full-slide image design. Use when the user asks to make a polished presentation, turn research, notes, or dictated direction into a deck, create an executive/client deck, build a deck with mood boards, or design slides using image generation. Requires the imagegen skill for mood boards and final slide images.
Persistent Obsidian-based memory for coding agents. Use at session start to orient from a knowledge vault, during work to look up architecture/component/pattern notes, and when discoveries are made to write them back. Activate when the user mentions obsidian memory, obsidian vault, obsidian notes, or /obs commands. Provides commands: init, analyze, recap, project, note, todo, lookup, relate.
Author and optionally direct-launch one durable loop-graph run for long-horizon agent work. Use when the user invokes /longgraph (or legacy /octopus), asks to design or start a multi-round run, needs durable state, gated milestones, owner decision boundaries, cross-host continuity, or independent audit. Detect Codex or Claude Code from context instead of asking. For a self-contained task, recommend the host's ordinary task or goal directly. Do not execute or resume existing runtime node files from this authoring skill.
Scaffold a new context-mill skill that walks an agent through an ordered series of narrowly-scoped reference files. Works for any number of steps. Use when the user wants to add a docs-only skill where the agent must read each step in strict order, one at a time, with each step isolated, with clear endpoints, and no prefetching.
Cut version bumps + changelogs for maintained packages in this monorepo. Detects which packages changed since their published npm version (git-vs-npm), reconciles against pending Changesets, runs `changeset version` to bump + cascade dependents, and opens a PR. This repo only produces version-bump commits; the npm publish is then triggered manually from a separate Databricks releases repo. Use when asked to "release", "cut a release", "bump versions", or "check what needs releasing".
> Compares the seller's own Mercado Livre listing against the available competing listings of the same catalog product — the buy-box competition — and says where they win, lose, and what to fix first. Use it when a seller asks why they don't win the buy-box, why they don't sell despite the same product, or how they rank inside a catalog, given a Mercado Livre or JoomPulse link or a listing or catalog product identifier. It scores each parameter — price, free shipping, Full, listing type, seller reputation, official store — as Melhor, Na média, Pior, or Criticamente pior, presents reviews and rating as context, and returns a verdict, a comparison table, prioritized actions, and a spreadsheet. Triggers include "why don't I win the buy-box" and the pt-BR "por que não ganho o buy-box". Mercado Livre (Brasil) only; sales and revenue are JoomPulse estimates, not real transactions. For a product and its competitors, use the single-product analysis skill; for one over time, the change-monitor skill.
> Finds new, already-selling product listings inside one Mercado Livre (Brasil) category on JoomPulse — recent listings (by default under about 30 days on air) that are well rated (rating above 3) and have real traction (more than about 30 estimated sales in the last month), all thresholds overridable — and returns them as a product table with a JoomPulse link per listing. Use it when a seller category", "what's launching and already selling", "recent best-sellers in my category", and the pt-BR equivalents "produtos novos na categoria", "novos anúncios que já vendem", "lançamentos em alta na minha categoria". Mercado Livre (Brasil) only; sales and revenue are JoomPulse estimates, not real transactions, while price, rating, and review count are real history. For weak-rated but well-selling products to beat, use the high-demand low-quality skill; for tracking one product over time, use the product change-monitor skill.
> Finds the fastest-growing deep sub-categories (leaf categories, deeper than the third level) under a chosen Mercado Livre (Brasil) category, ranked by month-over-month revenue growth, using JoomPulse market data. Use it when a seller picks a category and wants the niches inside it that are growing fastest growing in this category", "deep growing categories", and the pt-BR equivalents "subcategorias em crescimento", "nichos que mais crescem nesta categoria", "categorias profundas em alta". Sales and revenue are JoomPulse estimates, not real transactions. For one category's opportunity index, use the category-opportunity-index skill; for comparing category aggregates against a user-supplied previous snapshot, use the category-monitor skill; for new products inside a category, use the new-growing-products-in-category skill.
> Finds Mercado Livre (Brasil) products in one category that have high demand but a low rating — listings that sell well yet score at or below a rating threshold the user chooses, an opening to enter with a better offer. Use it when a seller wants weak-rated but well-selling products to beat. The skill asks for a category and a rating threshold, then returns the demand low rating products", "low quality opportunities", "products I can beat", and the pt- BR "produtos com muita demanda e nota baixa", "oportunidades de baixa qualidade", "produtos mal avaliados que vendem". Sales and revenue are JoomPulse estimates, not real transactions; price, rating, and reviews are real history. For brand-new listings use the new-growing- products-in-category skill; for niches without strong incumbents use the uncontested-niche- finder skill; for one product and its competitors use the ml-product-analysis skill.
> Reports the JoomPulse opportunity index (low, medium, or high) for one Mercado Livre (Brasil) category, alongside that category's current monthly market stats — estimated GMV, estimated sales, number of active sellers, number of active listings, and average ticket — then writes a plain-language read on how attractive the category is to enter. Use it when a seller wants a category worth entering", "show the opportunity index for this category", "how big is this market", and the pt-BR equivalents "qual o índice de oportunidade", "vale a pena entrar nessa categoria", "tamanho de mercado da categoria". Sales and revenue are JoomPulse estimates, not real transactions. For ranking the sellers in a category, use the top-sellers-in-category skill; for trending search terms, use the top-keywords-in-my-category skill; for comparing category aggregates against a user-supplied previous snapshot, use the category-monitor skill.
> Analyzes one Mercado Livre product and its competitors. Use this skill when a user wants to size up a single product or the products that compete with it — by a Mercado Livre link, a JoomPulse link, a photo, or a row of data. It returns a product card for the subject plus a ranked table of comparable products, each with price, estimated monthly sales and revenue, logistics, and does this sell", "find similar products", "find competing products", and the pt-BR equivalents "analisar este produto", "quanto vende esse produto", "produtos parecidos", "produtos concorrentes", "análise por foto", "análise por link". For "what new products should I add to my store" across a whole catalog, use the assortment gap-analysis skill instead — this skill analyzes a single product the seller already has in hand.
Claude Skills — questions
Answers built from the skills we actually parsed.