1,112 件 ・ 関連度順
概要と使いどころ
Review Stellar Community Fund (SCF) Build Award applications. Use when evaluating SCF submissions, covering project fit assessment, technical architecture evaluation, team readiness, traction validation, budget analysis, and funding recommendations.
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。
Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan related tasks.
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。
Design AI evaluation frameworks with benchmarks. TRIGGERS - Use when user needs help with ai-eval-framework related tasks.
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。
Build text and data classification systems with training and evaluation. TRIGGERS - Use when user needs help with ai-classification-system related tasks.
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。
Run a health check on an existing project: dependency audit, security scan, test runner detection, CI/CD evaluation, and missing configuration analysis. Maps the three execution gates (pre/in/post) from /10x-bootstrapper to an assessment framework for existing codebases. Reads optional context/foundation/stack-assessment.md from /10x-stack-assess to focus checks on identified gaps. Writes context/foundation/health-check.md with findings, prioritized fixes, and an agent-readiness verdict. Use when the user has an existing project and wants to verify its health before working with an agent. Trigger phrases: "health check", "check my project", "audit my project", "is my project healthy", "sprawdź projekt", "audyt projektu", "health-check", "project health". Use AFTER /10x-stack-assess (brownfield chain), BEFORE agent onboarding (m1-l4).
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。
Full pipeline: evaluation -> spec -> implementation -> review -> QA
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。
Debrief a finished Otameshi Tenshoku trial from the employer's side: set the observed facts against the completion criteria agreed before the start, keep expectations added afterwards, second-hand or off-the-job observations and the company's own late or missing support apart, settle the hours actually worked separately from any evaluation, and draft feedback to the candidate that states facts, support gaps and what happens next without promising a hire or continuation the company has not decided. Use when a hiring manager, recruiter or small-business owner wants to review a trial, decide what to tell the candidate, or work out the settlement after a shortfall; do not use to judge the candidate's aptitude or predict a hire, to cut pay or demand unpaid rework because of dissatisfaction, to compare candidates, or to send the feedback, pay, or update records on the user's behalf.
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。
Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.
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。
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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。
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
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。
Guides agents through scorecards, outcomes, model profiles, and eval suites. Use when running /cc-scorecard or /cc:scorecard, measuring a deliverable, or checking workflow gates with suite-run.
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。
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
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。
Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).
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。
High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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。
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
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。
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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。
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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。
Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.
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。
Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).
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。
Score an unbuilt business idea on desirability, viability, feasibility, distribution wedge, why-now, founder-market-fit, market size, alternatives, defensibility, capital intensity, and regulatory/ethical risk — and return a GO / ITERATE / KILL verdict with kill criteria and a next kill test. Load when the user asks to evaluate a business idea, score a startup idea, screen an idea, decide whether to pursue this venture, do an idea review, or says "is this a good business idea", "should I build this", "evaluate this startup", "screen this idea", "go/no-go on this idea", "kill or pursue". Sub-skill of `venture-exploration`. Calls `fermi` for sizing, `assumption-mapping` for hidden beliefs, optional `pre-mortem` / `adversarial-hat` for high-stakes ideas. Does NOT evaluate built products — for that use `reality-check`.
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。
Generate 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-evaluation`.
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。
Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.
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。