3,875 件 ・ 関連度順
概要と使いどころ
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。
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。
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。
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。
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。
Find, evaluate and install a ForgeCat profile. Use when the user wants to browse the registry, compare profiles, install one into a project or a home-target agent, or when an install is refused.
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 and evaluate grounded ideas. Use when the user asks for ideas, improvements, surprising options, or AI-generated directions before choosing one to develop; use ce-brainstorm to refine the user's own idea.
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 plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," "free resource," "ROI calculator," "grader tool," "audit tool," "should I build a free tool," or "tools for lead gen." Use this whenever someone wants to build something useful and give it away to attract leads or earn links. For downloadable content lead magnets (ebooks, checklists, templates), see lead-magnets.
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。
Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.
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-source research with citations using You.com. Use for questions that need several sources compared and synthesized, such as library or framework comparisons, tool and vendor evaluations, market research, or deep-dive reports. Routes between a cost-conscious agentic search loop and the one-shot `you-research` MCP tool.
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。
Verify AI-generated imports, API calls, and packages actually exist. Use when finishing AI-generated code that introduces new imports or external API calls — auto-fires after fix/cook to catch phantom functions, non-existent packages, and slopsquatting attacks.
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 codebase health assessment. Use when diagnosing project health or starting a rescue workflow on legacy code — OR when evaluating an external GitHub repo for dependency / fork / contribution decisions (--external mode). Analyzes complexity, dependencies, dead code, tech debt, and git hotspots. Produces a health score and rescue plan.
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 looking up journal impact factors (JCR IF), checking a journal's impact factor by name, comparing IF across journals, or answering questions about "影响因子" / "impact factor" / "IF". Triggers on "impact factor", "journal IF", "影响因子", "JCR", "IF score", "journal rank", "which journal has higher IF", "what is the IF of". PROACTIVELY USE when user mentions journal prestige, publication venue quality, or manuscript submission target 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。
Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.
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 interpreting research findings, evaluating scientific evidence, analyzing mechanisms, comparing competing hypotheses, designing experiments, or constructing scientific arguments.
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 biology grant ideas in the Chinese funding context (NSFC, MOST, etc.) — diagnosing project legitimacy, mechanism-centered scientific questions, reviewer-aware logic, innovation discipline, feasibility, and scope control across funding levels (youth, general, key).
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 grant ideas, diagnosing proposal logic, framing fundable projects, strengthening reviewer-aware arguments, or preparing to write any section of a research proposal.
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 ambient context artifacts (CLAUDE.md, memory, local skills, settings hooks) for compatibility with swarm governance. Returns a classified report so users can address interference before launching a team.
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 and measure Developer Relations programs across documentation, community, technical content, integrations, events and developer advocacy. Use when designing DevRel strategy, improving developer activation, launching an API/SDK, evaluating sponsorships, or connecting community activity to product adoption and revenue.
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 guidance for building node-based UIs with React Flow. Use when designing flow-based applications, making decisions about state management, integration patterns, or evaluating whether React Flow fits a use case.
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 auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.
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 the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: "research X for me", "do web research on", "look up sources for", "find citations for", "gather evidence on", "what does the web say about X". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups ("find this function", "search the repo"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced 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。