596 件 ・ 関連度順
概要と使いどころ
Use this when summarizing analysis results, writing reports, documenting experiment outcomes, or presenting model evaluation — including structuring findings in Japanese with conclusions, facts, assumptions, interpretations, and caveats.
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。
Map evidence-based connections between a wellbeing initiative and specific academic learning outcomes. Use when justifying wellbeing programmes, integrating SEL, or linking pastoral and academic 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。
Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.
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 metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.
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。
Recommends team composition based on outcomes and mode. Invoked by launch commands during team setup.
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。
General mode operational spec for the team lead. Returns lead identity, facilitator identity, suggest-members guidance, and phase arc for general-purpose teams. The silent fallback when outcomes fit no specific mode, and a wrapper base for custom workflows — it has no shortcut command.
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。
Independent review loop run before delivery in code work. An independent reviewer (Codex, or fresh Codex-style subagents) reads the whole PR against the approved outcome; the lead fixes in-scope functional findings and re-reviews until none remain. Its differentiator is independence and exhaustiveness — a reviewer that fails differently from the authors, run to clean — distinct from the recursive-refinement ladder. Invoked by the team lead from the unified pre-ship gate (code-mode and /swarm:refine) after the ship steps.
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。
Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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。
Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.
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。
Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.
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。
Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive process.
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/B testing, randomization, sample size calculation, confounding control, and causal inference for data science. Covers the full experimental lifecycle from hypothesis formulation through power analysis, randomization strategies, blocking, factorial designs, sequential testing, and the potential outcomes framework for causal claims. Use when designing experiments, planning A/B tests, calculating sample sizes, or reasoning about causation from data.
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 professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
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 primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.
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 primary outcome is creating publishable source-attributed content from URLs or supplied material, including research summaries, articles, briefs, and posts whose factual claims and quotations need reader-visible, verifiable citations.
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 primary outcome is creating a new portable Agent Skill directory from one local documentation file, documentation directory, or Git repository, including mixed text and capability-gated binary sources that require exhaustive source-to-output accounting.
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 primary outcome is refining an existing AI-facing artifact without dropping behavior, including SKILL.md, AGENTS.md, CLAUDE.md, rules, prompts, and agent definitions that need sharper invocation, structure, or completion criteria.
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 primary outcome is comparing documentation claims with implementation evidence, synchronizing docs from verified changes, or freshness review, including drift, missing coverage, stale claims, and post-change updates.
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 a diff needs four parallel perspective reviewers (Security, Performance, Quality, Accessibility). Creates an ephemeral SAM plan, collects structured verdicts, synthesizes them into one deduplicated cross-referenced punch list, prints one summary line per perspective, and exits non-zero if any perspective returns REJECT. SKIP is a passing outcome.
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 whether an implementation plan can achieve its stated outcome before execution. Use to check requirement coverage, task contracts, dependencies, data/artifact flow, testability, ownership, impact coverage, and executable ordering without checking whether implementation already exists.
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。
Scientific validation protocol for verifying fixes work through observation, not assumption. Use when claiming a bug fix, code change, refactoring, or implementation is complete — enforces reproduce-broken-state then define-success-criteria then apply-fix then verify-outcome. Success means observing intended behavior, not absence of errors.
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。
Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.
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。
Create, schedule, duplicate, inspect, cancel, archive, and troubleshoot Vapi outbound Campaigns. Use for persistent multi-contact calling, CSV or API contact personalization, campaign concurrency, campaign webhooks, pre-dial eligibility, contact outcomes, or rerunning an audience. Do not use for a single call or a simple one-off /call batch.
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。
Send feedback or bug reports to the Deepline team. Use when the user asks to report feedback or share a session, and proactively when a Deepline Play is disproportionately slow, appears stuck, or an ordinary outcome requires avoidable product steps.
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。