Reorient in the current Claude Code session with an evidence-backed map of its goal, pivots, outcomes, current step, and open 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。
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概要と使いどころ
Reorient in the current Claude Code session with an evidence-backed map of its goal, pivots, outcomes, current step, and open 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。
Create professional investment proposals for prospective clients. Covers the firm's approach, proposed allocation, expected outcomes, and fee structure. Use when pitching new clients or presenting a new investment strategy. Triggers on "investment proposal", "prospect presentation", "pitch new client", "proposal for [client]", or "new client presentation".
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 planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical statistical rigor.
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。
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as 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。
Create comprehensive test scenarios from user stories with test objectives, starting conditions, user roles, step-by-step actions, and expected outcomes. Use when writing QA test cases, creating test plans, defining acceptance tests, or preparing for feature validation.
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。
Reconstruct a daily or weekly worklog from observed activity and 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。
Summarize one project against its stated milestones using recent work and verified 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。
Weekly win-loss analysis for HubSpot closed deals. Reads deals closed-won and closed-lost in the trailing {{lookback_days}}-day window, breaks outcomes down by segment, competitor, price, and the stage lost deals die in, clusters close reasons into themes, and posts themes plus recommendations to {{alert_channel}}. Read-only and report-only — never writes to a HubSpot deal.
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。
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
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。
Emit the complete Resolve handoff with exact modes, outcomes, test evidence, and publication state.
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 replacing a seat's occupant (rebuild/handover/swap), reasoning about stable-seat-identity vs fluid-occupant-identity, choosing an old-occupant disposition (retire/advise/shadow), or recording provenance for an occupant change. Two independent outcomes (continuityOutcome + seatBindingOutcome) and the 5 failure modes that prevent silent dishonesty.
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 converting PM intake into a slice SPEC.md with observable acceptance outcomes, explicit scope, and advisory sibling build dependencies.
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 an AI product idea across outcomes, hypotheses, risks, and positioning. Use when deciding whether an AI solution deserves investment or recommendation.
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 user stories with Mike Cohn format and Gherkin acceptance criteria. Use when turning user needs into development-ready work with clear outcomes and testable conditions.
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 an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.
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。
Investigate Stripe payment failures and unexpected outcomes from exact object IDs, lifecycle state, events, amounts, currency, refunds, and dispute 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。
Record final outcomes to history.md, not intermediate requests or reversed 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。
Design and verify a deterministic operational-value grader for any GitHub Agentic Workflow. Use when reasoning from workflow goals to measurable downstream outcomes, defining repository evidence, choosing outcome metrics, or creating an operational-value evaluator. Usage: /operational-value-designer OWNER/REPO WORKFLOW-NAME.
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。
STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.
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。
Statistical significance calculator for A/B test results with sample size requirements, segment breakdowns, and hypothesis generation. Use when feeding test results, checking statistical significance, calculating sample sizes, analyzing experiment outcomes, or generating next test ideas based on results. Platform: Google and Meta.
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。
Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.
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。
Conversational AI-first interface with minimal controls, clear outcomes, and delegated task flows for agentic workflows.
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。
Analyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.
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。