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Found 243 Skills
Pre-logic validation tool that proactively identifies logical flaws before providing answers, used for rationality verification in scenarios such as product recommendation, quotation, data analysis, etc. Use when: - Validate product price authenticity before product recommendation - Data analysis consistency check - Budget-plan matching validation - Unit conversion verification - Factual statement validation before making factual claims - Proactively find logic flaws Cross-references: content-extractor, long-form-writer, rss-feed, document-hub Part of UniqueClub toolkit. Learn more: https://uniqueclub.ai
Production-grade Playwright testing toolkit. Use when the user mentions Playwright tests, end-to-end testing, browser automation, fixing flaky tests, test migration, CI/CD testing, or test suites. Generate tests, fix flaky failures, migrate from Cypress/Selenium, sync with TestRail, run on BrowserStack. 55 templates, 3 agents, smart reporting.
UI/UX design optimization toolkit with 20 built-in themes and custom theme generation. Applies design systems with color, typography, spacing optimization for any tech stack. Use when user says "rico optimize", "rico 优化设计", "rico use style", "rico 用风格", "rico theme","rico 应用主题", "rico 生成主题", "rico design".
Use this skill when the user says "/li", or when the request is related to content creation but the intent is ambiguous and the user is unsure which tool to use. As the main entry point of the li Toolkit, it judges the intent and routes to the corresponding specialized skill. Do NOT trigger: When the user's request clearly matches a specific li-* skill (e.g., "write a script" → li-writer, "deepen a topic" → li-topic), directly trigger the corresponding skill instead of this entry. DO NOT trigger for non-content-creation tasks. Use when the user says "/li" or the intent is ambiguous across multiple li-* tools.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
EDA toolkit. Analyze CSV/Excel/JSON/Parquet files, statistical summaries, distributions, correlations, outliers, missing data, visualizations, markdown reports, for data profiling and insights.
Market and competitive analysis toolkit. Research competitors, analyze market positioning, identify differentiation opportunities, and create comprehensive competitive landscape assessments for software projects.
Comprehensive toolkit for validating, linting, testing, and automating Ansible playbooks, roles, and collections. Use this skill when working with Ansible files (.yml, .yaml playbooks, roles, inventories), validating automation code, debugging playbook execution, performing dry-run testing with check mode, or working with custom modules and collections.
Debugging toolkit for AI agents. Diagnose symptoms via memory cache -> behavior cache -> codebase search, trace data flow, git-bisect bad commits, and compare output directories.