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概要と使いどころ
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive 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。
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.
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。
Free tool marketing strategy covering idea evaluation, tool design, lead capture architecture, SEO landing pages, launch playbook, and ROI measurement for calculators, generators, checkers, graders, and interactive 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。
Design strategic partnerships — technology, channel, co-marketing — and the programs that scale them. Use when evaluating a partner, picking a partnership type, designing a partner program, structuring a deal, or modeling ROI.
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。
Data leadership advisor on data strategy, governance, quality, and platform decisions. Use when defining a data strategy, scoring data maturity, auditing data governance, evaluating a data platform, or designing the data org.
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。
Revenue leadership for B2B SaaS: forecasting, sales model design, pricing, and net revenue retention. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, or scaling sales teams.
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。
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, or making architecture decisions.
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 creating, improving, finding, or auditing agent skills - the user says 'create a skill', 'do I have a skill for X', 'improve the X skill', 'which skill should I use', asks whether a skill exists for a task, or wants to validate, test, evaluate, package, or health-check skills. Also use for skill ecosystem maintenance (duplicate detection, stale skills, trigger collisions) and advisor checkpoints.
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 emerging channels such as voice or social commerce with pilot plans. "should we open a TikTok Shop"
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 a batch of content in one run: ranked quality report, systemic issues. "score our whole content library"
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。
Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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 a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.
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 scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent 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。
After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.
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。
Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.
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。
Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
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。
Provide graduated assistance from abstract conceptual nudge to concrete procedural step, with reflection required before each escalation. Teaches help-seeking as a skill and prevents direct-answer shortcuts.
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。
The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.
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 a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.
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 a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
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 a structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.
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。
Audit an AI agent skill before installing it. Use proactively whenever the user is about to add, install, enable, or evaluate an unfamiliar skill — including phrases like "audit this skill", "is this skill safe", "scan skill before install", "check skill for malicious code", "review this plugin", or any pre-install gate on an untrusted, third-party, or community-distributed skill bundle. Runs ten categories of static checks (code execution, network exfiltration, credential harvesting, persistence, prompt injection, supply-chain hooks, obfuscation, destructive filesystem ops, secrets, Trojan Source / homoglyphs) and emits a PASS / WARN / FAIL verdict.
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。