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概要と使いどころ
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.
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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。
Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.
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。
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 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。
Respond to review comments on a PR after evaluation and fixes
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 a cited, structured read of local documents and project knowledge. Triggers on: "analyze these docs", "scan my project for context", "read the docs folder", "summarize what's in .beagle/concepts/", "extract context from docs/", "what's in this folder", "go read everything in X and tell me what's there". 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"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan 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。
Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".
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。
Insurance program review, coverage adequacy testing, contractor insurance verification, builder's risk/OCIP/CCIP evaluation, and property tax escrow management for AM, PM, and Development.
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。
Insurance policy analysis, claims evaluation, coverage assessment, and risk management for individuals and businesses
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Generates a structured initial contact summary memorandum from a potential client's first interaction with the firm. Captures case facts, liability indicators, damages, insurance details, and screening criteria. Use during client intake to support conflict checks, case evaluation, and engagement decisions.
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Expert knowledge of Behandlingsresultat (treatment result) domain in melosys-api. Use when: (1) Understanding result types and their meaning (FASTSATT_LOVVALGSLAND, MEDLEM_I_FOLKETRYGDEN, etc.), (2) Working with periods - Lovvalgsperiode, Medlemskapsperiode, Anmodningsperiode, Utpekingsperiode, (3) Debugging vedtak/decision metadata, criteria evaluation, or social insurance charges, (4) Understanding EU/EEA coordination logic and Article 16 exception handling, (5) Working with trygdeavgift (social insurance charge) calculations.
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。
Structures cyber insurance evaluation with coverage assessment, limit adequacy, and claims scenario analysis. Use when evaluating cyber coverage, analyzing policy terms, or assessing cyber insurance adequacy.
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 existing eval coverage — gaps, metric validity, benchmark leakage, dataset freshness. Use when asked "what eval coverage do we have", "audit our evals", or "check for benchmark leakage".
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。
Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".
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 an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".
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 automated regression suites — golden sets, threshold alerting, CI integration for model changes. Use when asked to "catch model regressions in CI", "build a golden set", or "automate eval regression".
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 eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".
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。
Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.
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 online information for credibility, accuracy, and context. Covers the SIFT method (Stop, Investigate, Find, Trace), lateral reading, reverse image search, primary source verification, and common misinformation tactics. Use when assessing the trustworthiness of a web page, article, video, social media post, or any digital claim before relying on it or sharing 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。
Supervised and unsupervised learning, bias-variance tradeoff, cross-validation, decision trees, ensemble methods, neural network fundamentals, and the practitioner's workflow from problem framing through deployment. Covers classification, regression, clustering, dimensionality reduction, regularization, hyperparameter tuning, and evaluation metrics. Use when building predictive models, selecting algorithms, or understanding the machine learning pipeline.
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。
🐟 Rust-native Fish shell-friendly file operations with Steel-backed SCI Clojure 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。
Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.
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。