Queries 20+ bioinformatics resources through CLI/Python. Supports quick lookups of gene info, BLAST/BLAT, viral sequence downloads, PDB/mmCIF structures, G2P residue annotations, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
Guides protocol selection, input preparation, pricing checks, and browser ordering on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio). Applies to cell-free, E. coli, and Pichia protein expression; HiBiT, A280, and LabChip readouts; IVT mRNA/circRNA synthesis; thermal shift assays; Echo-MS methods; SPR target onboarding; plate-reader assay onboarding; and fluorescent pixel art.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
Prepares bulk RNA-seq FASTQ, Salmon, STAR or featureCounts output for gene-level differential expression. Covers nf-core/rnaseq and standalone quantification, biological replication, strandedness, reference provenance, validated count assembly and a PyDESeq2 handoff. Use for FASTQ-to-counts analysis, nf-core/rnaseq configuration, STAR/Salmon quantification, or building a counts matrix for DESeq2. For single-cell data use scanpy; for statistical fitting alone use pydeseq2.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
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.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.
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。
K-Dense-AI/scientific-agent-skills☆ 4.9万2026年10月12日 更新
Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app
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。
github/awesome-copilot☆ 4万2026年10月12日 更新
Scans GitHub Actions workflows and CI/CD pipeline configurations for supply chain attack vectors including unpinned actions, script injection via expressions, dependency confusion, and secrets exposure. Uses PyGithub and YAML parsing for automated audit. Use when hardening CI/CD pipelines or investigating compromised build systems.
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。
mukul975/Anthropic-Cybersecurity-Skills☆ 3.4万2026年8月31日 更新
Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.
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。
mukul975/Anthropic-Cybersecurity-Skills☆ 3.4万2026年8月31日 更新
Generate synonyms for words or phrases. Use this skill when the user needs alternative words with similar meanings, wants to expand vocabulary, or seeks varied expressions for writing.
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。
QwenLM/qwen-code☆ 2.8万2026年10月13日 更新
Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.
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。
czlonkowski/n8n-mcp☆ 2.3万2026年10月13日 更新
Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
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。
google/skills☆ 2.1万2026年10月13日 更新
Regular expression expert for crafting, debugging, and explaining patterns
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。
RightNow-AI/openfang☆ 1.8万2026年7月2日 更新
Audit a datafusion-spark function implementation for correctness against Apache Spark 4.2.0. Studies the Spark source across versions, reviews the Rust implementation and its signature, verifies expected values against a real PySpark, fixes divergences, and captures the rest as issues and disabled tests.
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。
apache/datafusion☆ 9,4442026年10月14日 更新
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
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。
trailofbits/skills☆ 7,5112026年10月10日 更新
Comprehensive CI/CD pipeline exploitation methodology covering GitHub Actions injection vectors (expression injection via PR titles and issue bodies, workflow_run event abuse, GITHUB_TOKEN over-scoping, composite action supply chain compromise), Jenkins attack paths (Groovy sandbox escapes, script console remote code execution, Java remoting deserialization, credential store dumping, shared library injection), GitLab CI exploitation (YAML anchor injection, runner registration token abuse, CI variable extraction, protected branch bypass via merge request pipelines), and Azure DevOps pipeline agent compromise with service connection theft. Includes artifact poisoning techniques across all platforms, tooling guidance for gato and jenkins-attack-framework, and maps to MITRE ATT&CK T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Covers enumeration of pipeline configurations, privilege escalation from contributor to code execution, lateral movement through pipeline trust boundaries, and persistence via modified workflow definitions. Each technique section provides working exploitation code, detection indicators, and defensive countermeasures.
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。
SnailSploit/Claude-Red☆ 7,4792026年10月13日 更新
Dispatch bulk RNA-seq requests to FASTQ-to-count QC or count-matrix differential-expression skills using nf-core/rnaseq, STAR, Salmon, featureCounts, MultiQC, and R/Bioconductor workflows.
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。
openai/plugins☆ 7,4312026年10月8日 更新
Submit compact Bgee SPARQL requests for healthy wild-type expression metadata and ontology-aware lookup patterns. Use when a user wants concise Bgee summaries; save raw results only on request.
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。
openai/plugins☆ 7,4312026年10月8日 更新
Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.
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。
czlonkowski/n8n-skills☆ 6,4162026年10月9日 更新
Search a codebase efficiently with ripgrep regular expressions, file globs, and git history search. Use to locate symbols, usages, and definitions instead of reading whole files.
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。
agentscope-ai/agentscope-java☆ 5,9562026年10月14日 更新
This skill should be used when the user asks for efficient communication, task reports, file-operation summaries, research discussion, study-note synthesis, planning, writing feedback, or responses that need conclusion-first structure, concrete evidence, risk disclosure, and useful next steps.
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。
Galaxy-Dawn/claude-scholar☆ 5,7732026年9月23日 更新
Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.
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。
dotnet/skills☆ 5,6202026年10月13日 更新
Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.
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。
dotnet/skills☆ 5,6202026年10月13日 更新
Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3) Prioritising non-coding variants by predicted track delta.
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。
aipoch/open-science☆ 4,8592026年10月13日 更新