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概要と使いどころ
Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor CLI.
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Benchling Python SDK and REST API integration for registry entities, inventory, ELN entries, workflows, Benchling Apps, and Data Warehouse queries. Use when automating lab data with benchling-sdk or the v2 API.
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Analyzes flow cytometry data with FlowKit, including spillover compensation, logicle and biexponential transforms, hierarchical gating, GatingML strategies, and supported FlowJo 10 workspaces. Use for reproducible gate counts, population percentages, gated fluorescence summaries, or reproducing a FlowJo analysis in Python. For FCS metadata inspection or file-format repair alone, use FlowIO.
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Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.
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Queries the CZ CELLxGENE Census programmatically for versioned public single-cell and spatial transcriptomics data. Use when you need population-scale cell metadata, gene expression slices, Census summary counts, source H5AD URIs/downloads, embeddings, spatial Census data, or reference atlas comparisons across organisms, tissues, diseases, assays, and cell types. For analyzing your own local single-cell data use scanpy, anndata, or scvi-tools.
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Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.
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Audit or improve manuscript statistical reporting, including experimental units, replication, uncertainty, tests, and figure statistics. Use for 统计审查、统计方法小节、图注统计 and reviewer concerns; compute new analyses only when requested with data.
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Use when querying or extending GitNexus's PDG control/data-dependence surface (the `pdg_query` MCP tool, CDG/REACHING_DEF edges), or reasoning about "what controls X" / "where does Y flow" / guard clauses. Examples: "what guards this statement?", "trace this variable within the function", "why is the pdg_query result empty?", "add a CDG query".
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Use when working on, reviewing, or extending GitNexus's CFG/taint/PDG subsystem (the `--pdg` layers), or when reasoning about source→sink data-flow findings. Examples: "How does taint analysis work here?", "Why didn't explain find this flow?", "Add a new sink/source", "Review the interprocedural taint code".
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Use when working in a project that depends on @prisma/orm-postgres, @prisma/orm-sqlite, or @prisma/orm-mongo (Prisma 8, formerly Prisma Next): editing contract.prisma or a contract.ts builder, running `prisma contract emit`, planning or applying migrations, editing migration.ts, writing db.orm / db.sql / db.query queries, wiring db.ts or middleware, integrating a build tool, using the Supabase extension or RLS, or reading a dotted error code such as MIGRATION.HASH_MISMATCH. Use when the user asks "what is Prisma 8", "where do I start", or compares it to another ORM. Use when the user asks to upgrade or bump Prisma 8 in an app or an extension package. Use when you see @internal/* or @prisma/orm-* imports, prisma.config.ts with definePrismaConfig, or contract.json / contract.d.ts. Do not use for Prisma ORM 7 or earlier (schema.prisma + @prisma/client).
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Daily attendance register: check-in and check-out, hours worked, work mode, late minutes, leave and regularisation flags, as CSV, SQL, JSON Schema or Notion on request. Use for payroll input.
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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
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AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
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Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.
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Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.
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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
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Alumni and re-hire register: former role, last working day, re-hire eligibility, current employer and re-engagement date, as CSV, SQL, JSON Schema or Notion on request. Use for alumni outreach.
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Announcement board: author, category, department, priority, audience, publish and expiry dates, status and acknowledgements, as CSV, SQL, JSON Schema or Notion on request. Use for internal notices.
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When the user wants to set up, improve, or audit analytics tracking and measurement.
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Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
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Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.
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Build natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.
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