Data & AI skills

ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.

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Guidance for SPR and BLI binding characterization experiments. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.

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Workflow for untargeted or targeted metabolomics including preprocessing, normalization, annotation, statistics, and pathway mapping.

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Lists tables, describes columns and data types, identifies foreign key relationships, and maps entity relationships in a database. Use when the user asks about database schema, table structure, column types, what tables exist, ERD, foreign keys, or how entities relate.

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Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.

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Call the free Agnes text model API for chat completions, streaming answers, coding help, tool-calling experiments, and OpenAI-compatible text generation. Use when the user asks for 免费文本, 免费的文本模型, free text API, Agnes text, Agnes-2.0-Flash, free LLM calls, or wants to use a free chat/completion model through an Agent skill.

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Find and qualify new prospects with Firecrawl Alexandria people and company data: people by role, seniority, company, industry, size, location or technology, target accounts that match criteria, or the right contacts at companies, returned as a ranked, sourced table or CSV. Also extracts lead lists from a database or directory the user names, and refreshes a prospect list with only new matches. Use when the user wants a lead list, prospects, target accounts or the people to reach out to at companies that fit criteria. Not for enriching people or companies the user already supplied (use firecrawl-lead-research).

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Researches products across retailers and review sites with Firecrawl and recommends what to buy within the user's budget and preferences, with current prices and sources. Use when the user wants to compare products, find the best option or check reviews before buying, since prices and models change faster than training data. Adds to a cart only when asked and a signed-in browser profile exists.

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Build a knowledge base from web content with Firecrawl. Use for local reference docs, RAG-ready chunks, fine-tuning datasets, documentation mirrors, topic corpora, or LLM-ready markdown organized from web sources.

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Read metrics off web dashboards with Firecrawl browser: public stats, status and transparency pages as well as analytics tools the user is signed in to. Use when the user wants numbers, KPIs or charts from a dashboard turned into a report, summary or date-range table, even without an explicit request to scrape.

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Analyze influencer performance metrics across TikTok, Instagram, YouTube, Bilibili, and Kuaishou. Compare engagement rates, audience growth, content consistency, and cross-platform presence for data-driven partnership decisions.

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Enrich any entity — companies, domains, emails, or people — with verified data from multiple sources. Combines Apollo, Strale, LinkedIn, and Akta data to fill gaps in your CRM, lead databases, or research datasets with production-quality enrichment.

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Apply validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.

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Design production-ready music prompts and optionally generate music through SandBase. Use for songs, instrumentals, soundtracks, jingles, loops, or multi-part music; use a speech or sound-effect workflow when music is not the main deliverable.

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Apply sql optimization patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql optimization patterns or its strengths fit the task.

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Analyze estimated website traffic, engagement, rankings, channel mix, and geographic distribution through SandBase. Use for domain benchmarking, acquisition-channel analysis, or traffic trend research; do not present third-party estimates as first-party analytics.

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Apply statistical power in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statistical power or its strengths fit the task.

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Apply sql queries in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses sql queries or its strengths fit the task.

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Plan, implement, or review ml pipeline work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests ml pipeline work.

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Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.

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Apply data visualization in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data visualization or its strengths fit the task.

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Apply data context extractor in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses data context extractor or its strengths fit the task.

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Use when designing, refactoring, or debugging Formax system prompt assembly, system-reminder injection, or skills/deferred-tool prompt exposure semantics; mandatory for any change involving FORMAX_DEFERRED_TOOL_EXPOSURE and request-payload parity checks.

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Structured persuasion for tech leads, PMs, and founders—not activity logs. Five scenarios (kickoff, status update, wrap-up, investor pitch, solution selling) on one 5-part framework (Hook→Context→Proposal→Evidence→Ask). AI prompts for missing materials and audience context; pre-submit checklist. Claude Code plugin; Cursor, Codex, and chat via prompts.

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