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概要と使いどころ
Use when a user wants to configure and launch a WorkBuddy Bench evaluation run from scratch, or is unsure how to set one up. Walks them interactively through environment setup, model config, .env credentials, job config, and launching the run, then hands off to wbbench-report-skills for metrics and trajectory analysis.
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 when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.
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。
Ask which skill or flow fits your situation. A router over the skills in this repo.
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。
PL (Project Lead) role sub-skill for incident analysis and process evaluation. Loaded through the pl core routing table, or when the owner explicitly asks for a retrospective or root-cause write-up; NEVER auto-load from ambient context.
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。
Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.
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。
Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.
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。
Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.
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。
Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.
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。
Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.
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。
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
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。
Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.
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。
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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。
Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.
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 when evaluating a newly adapted model's accuracy/eval score. Runs evalscope eval (or the test/srt/run_eval.py fallback) against an OpenAI-compatible endpoint on datasets like gsm8k / aime, producing per-dataset reports and a score. Accuracy only — throughput/latency benchmarks belong to model-speed-benchmark. Triggers on accuracy, eval, evalscope, gsm8k, aime, run_eval — not throughput.
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 chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.
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 comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.
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 fault tolerance patterns for Spring Boot 3.x using Resilience4j. Use when implementing circuit breakers, handling service failures, adding retry logic with exponential backoff, configuring rate limiters, or protecting services from cascading failures. Generates circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback implementations. Validates resilience configurations through Actuator endpoints.
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 patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.
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。
Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.
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。
When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.
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。
When the user needs to design or evaluate system architecture — service boundaries, data models, API contracts, infrastructure topology, database selection, or dependency analysis. Also activate for "design the system", "how should I architect this", "monolith vs microservices", or architecture decision records.
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。
When the user needs to evaluate competitors, understand the competitive landscape, or position their product against alternatives.
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。
When the user needs to review an existing contract, assess risk in proposed terms, or evaluate a contract before signing.
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。
When the user wants to identify, evaluate, or prioritize potential investors for a fundraising round. Also activates when the user asks "who should I pitch?", "find me investors", "build an investor list", or mentions VC/angel targeting.
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。