788 件 ・ 関連度順
概要と使いどころ
Trace and resolve discrepancies when the same metric shows different values in two or more sources. Use before reporting, after pipeline changes, or when stakeholders question a number.
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。
Converts audio objects stored in Volcengine TOS into PCM-oriented WAV output through TOS data-process async jobs. Use this skill when the user needs to convert audio to PCM or WAV, resample audio to 16k, convert to mono, change sample format, normalize audio for ASR pipelines, or prepare TOS audio files for speech recognition — even if they don't explicitly mention 'PCM' or 'audio conversion'.
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。
LEAP builds skills through two pipelines: Branch A distills a skill from raw data, while Branch B combines multiple skills into one. It is called by the main SkillAlchemy workflow. Use when SkillAlchemy requires distillation or fusion.
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。
Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.
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。
Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".
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。
Diagnoses product-market fit from survey, usage and pipeline data and returns a scored verdict with the next moves. Use when you ask 'do we have product-market fit', 'is our PMF survey good enough', 'what does 35% very disappointed mean', 'should we scale yet', 'why is growth stalling', or need a Sean Ellis survey, retention check, or PMF level read. Draws on 137 insights from 66 Lenny's Podcast guests incl. Sean Ellis, Todd Jackson and Rahul Vohra.
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。
Trigger for: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data alone.
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。
Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing 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。
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
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。
Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics pipelines. Supports multiple formats.
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。
Three-tiered approach to omics data analysis (transcriptomics, proteomics) covering validated pipelines, standard workflows, and custom methods
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。
smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock; for the Python-bindings/Vinardo workflow use autodock-vina-docking.
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。
Programmatic PubMed access via NCBI E-utilities REST API. Covers Boolean/MeSH queries, field-tagged search, endpoints (ESearch, EFetch, ESummary, EPost, ELink), history server for batches, citation matching, systematic review strategies. Use for biomedical literature search or automated pipelines.
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。
Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.
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。
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
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。
Scaffold a new SciAgent-Skills entry. Picks pipeline/toolkit/database/guide template, creates skills/{category}/{name}/SKILL.md with valid frontmatter, appends the registry.yaml entry, runs validation. Enforces name uniqueness, kebab-case, description keyword rules, schema rules from AGENTS.md. TRIGGER when user says (any language): "add a SciAgent skill", "add a skill for <X>", "create new skill", "create a SKILL.md for <X>", "scaffold a skill", "new skill entry", "register a skill", "신규 skill 추가", "스킬 만들어줘", "스킬 생성", "skill 만들어", or any request to add a new SKILL.md to this repo. ALWAYS invoke this skill BEFORE writing to skills/ or registry.yaml. DO NOT TRIGGER when: editing existing entry's content (just edit the file directly); migrating an existing entry (read AGENTS.md "Migrating from Existing Entries" first); only updating registry.yaml without creating a new SKILL.md.
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。
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
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。
Read, decompose, and analyze IEEE-style papers using routed technical-reading workflows. Use when extracting problem setting, object-method-condition logic, contributions, method pipeline, equations, datasets, baselines, metrics, ablations, limitations, replication details, citation role, related-work positioning, implementation notes, or reviewer-style assessment from a paper, PDF, abstract, or manuscript excerpt.
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。
Operational knowledge for the daily-brief digest pipeline (this project). RSS/API fetchers, pluggable LLM enrichment (default claude CLI on Max; also anthropic/openai/deepseek/minimax API), trading section, HTML rendering, cross-platform scheduler integration (Windows Task Scheduler / macOS launchd / Linux cron). Load when the user asks about running daily / regenerating sections / debugging a failed run / adding or disabling sources / LLM quota / scheduler / why a tab shows wrong data / why a source failed / switching LLM backend. Always prefer the documented npm commands over re-implementing logic. Diagnose by reading logs/daily-*.log first, then logs/llm-calls.jsonl for LLM-side issues.
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。
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI 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。
Create n8n workflow for daily task assignment from PTO engineer to foreman via Telegram bot with status reporting.
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。
Use FFCV to convert datasets into .beton files, build fast PyTorch loader pipelines, handle image and custom fields/transforms, and tune cache, traversal, and throughput behavior.
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。
Operate the Fengshenbang-LM Fengshen package for pipelines, model families, data/training utilities, examples, and conversion planning.
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。
Use FlashRAG for retrieval-augmented generation research workflows: config/data validation, retrieval and index building, generation/refinement components, RAG pipelines/method reproduction, evaluation, and WebUI setup.
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。