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概要と使いどころ
Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.
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Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a
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Reactome is a free, open-source, curated and peer-reviewed pathway database. Their goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge.
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Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.
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Expert knowledge for Azure Service Connector development including troubleshooting, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring Azure compute to Redis, databases, storage, messaging, AI services, or third‑party endpoints, and other Azure Service Connector related development tasks. Not for Azure API Management (use azure-api-management), Azure Connector Namespace (use azure-connector-namespace), Azure Logic Apps (use azure-logic-apps), Azure Service Bus (use azure-service-bus).
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Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".
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Compare reply rates, bounce rates, and positive reply rates broken down by inbox type (SMTP / Gmail / Outlook) for a Smartlead account. Use when you want to know "which inbox type delivers best" or when debugging unexplained deliverability differences across a mixed fleet. Operates via Smartlead API only (no database required).
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Find lookalike companies via DiscoLike's 65M+ business domain database. Search by seed domains ("find companies like clay.com and apollo.io") or natural-language ICP text ("B2B cold email outreach"). Supports negation domains (exclude competitors/existing customers) and country filtering. Use when you already know 3-10 reference companies and want hundreds more that look like them. Outputs CSV ready for /blitz-list-builder or the email waterfall.
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Autonomous cold email campaign launcher. Takes one target company domain, scrapes their website, generates an ICP with Claude, pulls matching leads via Prospeo, enriches emails + company descriptions, personalizes each lead with parallel Claude Code Task sub-agents (A/B/C variants), and uploads as a live Smartlead campaign. Uses local JSON files for state (no database required). Use for automated daily campaign launches after you have an initial `client-profile.yaml` from /icp-onboarding. Triggers on "auto-research", "launch an automated campaign", "daily campaign", "run the research loop".
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Turns one customer case study into the tightest list of companies that would read that case study and see themselves. Decomposes WHY the case-study company resonates into concrete attributes, translates those attributes into database filters, intersects them with a lookalike anchor, then widens with a model judge. Triggers on "find more companies like our best customer", "who would our case study land with", "lookalikes of X", "build a list around this case study", "companies like our happiest client", "expand this list, it is too small". Outputs a filter set plus one merge field naming which case study to cite.
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Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".
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Write safe, reversible database migrations for this repo's conventions. Use for any schema change.
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Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.
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Detect and investigate bulk registration abuse on SaaS platforms. Given access to a user database (or exported CSV/JSON), run a multi-dimensional anomaly analysis — email domain clustering, registration tempo, UA fingerprinting, session structure, usage patterns, and credit consumption — to produce a scored abuse report with actionable recommendations. Use when investigating suspicious sign-up spikes, credit fraud, free-tier abuse, or account farming.
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Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.
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Weekly. Confirms that the registrar, the host, and the hosted database still match what the inventory says, in one read only pass per project, and names every drift with the exact screen, the exact current value, and the exact intended value. It records that a variable exists and never its value, it fixes nothing inside an account, it buys nothing in any state, and it never rotates a key.
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Monthly, and on its first run it is the whole setup, launched by hand. It discovers what the member actually runs by walking their code roots and reading the registrar, the host, and the hosted database, then writes the inventory, the domains file, the starting budgets, and the opening safe fix rules, seeds the board, builds the dashboard, and registers one scheduled job per routine. It creates no account, buys nothing, and provisions nothing.
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Database security (encryption, access control, injection prevention), data governance (lineage, quality, MDM), and compliance frameworks (GDPR, CCPA, HIPAA)
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SQL and NoSQL query optimization techniques, indexing strategies, execution plan analysis, JOIN algorithms, cardinality estimation, and database-specific query patterns
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Domain-specific authoritative source databases, search strategies by topic category, and source freshness rules
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Use when adding server-side API routes to an Expo Router app (+api.ts files) for secrets, database access, third-party proxies, webhooks, CORS, or EAS Hosting deploys. Covers file routing, env vars, and Cloudflare Workers limits.
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Turn a complete beginner's app idea into a buildable plan, then keep them oriented while they build. Use it whenever someone who has never coded wants to build or "vibe code" an app, has an idea but no idea where to start, or wants it turned into a plan, MVP scope, tech stack, user flows, or blueprint. It has three on-ramps: the full journey, a validate-only pass ("is my idea worth building", "reality-check this idea"), and a plan-only path for someone arriving with validation done ("I already validated my idea", "I have the research, just plan my MVP", "skip the research"). ALSO use it when a non-coder needs build-time basics: what Git and GitHub are, making an account, "commit and push," local vs. staging vs. production, putting an app online (deploy/ship), or keeping API keys safe. AND use it in Checkup Mode when someone who built with AI says it became a mess, the AI keeps breaking things or going in circles, they're scared to touch their code, or they ask "is my code organized" or "can you clean it up." Built for people who don't know what an API, database, or GitHub is, so reach for it when they never say "plan" or "architecture." Not for an experienced dev debugging, refactoring, or setting up CI/CD.
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Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services.
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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
日本語の概要は準備中です。原文の説明を表示しています。