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概要と使いどころ
Observational cosmology from Hubble's law to the CMB. Covers redshift, Hubble expansion, the cosmological parameters, the cosmic microwave background, large-scale structure, galaxy rotation curves and dark matter, Type Ia SNe and dark energy, and the current state of Lambda-CDM. Use when reasoning about the large-scale universe, interpreting cosmological surveys, or teaching the Big Bang evidence chain.
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。
用户写完推文初稿、准备发布前做完备性自检时;或纠结"这条推文发出去会有人收藏吗 / 值得收藏吗"时;或感觉初稿"差点什么"但说不清缺什么时。关键 trigger: 检查推文还缺什么 / 五件套 / 完备性检查 / 收藏理由 / 证据够不够 / 三个可 / checklist / is this tweet complete / worth saving。不适用于:还没写初稿时(先用起草工具);评估值不值得发(那是四省模型)。本 skill 把推文当小型信息产品逐件自检五要素:价值承诺 / 使用场景 / 低门槛入口 / 证据(三个可:可见可点可算)/ 收藏理由,缺任一件读者在该环节流失。
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。
Records hypotheses, evidence and decisions when proposing, running or evaluating forecast experiments, including backtests and feature or model comparisons. Excludes routine tests and unchanged forecast operation.
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。
当用户需要用客观数据或直观实验来证明产品性能时调用此 skill。适用场景：产品有可量化的性能优势（如更强、更轻、更持久）、需要视觉化展示产品实力、消费者对产品功效有理性疑虑。不适用于：纯情感型产品（如礼品）、无法量化的体验（如口感）。关键 trigger 信号：用户提到"数据证明""实验对比""性能参数""效果演示""直观展示"。核心方法分两路：直接证明（精确数据 + 链接到熟悉事物）和间接证明（设计实验创造视觉对比）。
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。
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing. Produces professional scorecard with evidence-based ratings and actionable recommendations. (project, gitignored)
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 the user asks what to do next, asks for a plan under uncertainty, challenges stalled or low-trust work, requests multiple possible approaches before action, or needs an evidence-first loop that uses RT-ICA-style prerequisite checks, adversarial review, subagent coordination, real validation, and durable concern tracking.
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 files or a requested directory scope from actual contents. Extract code, config, data and document evidence, preserve exact counts and source locations, and disclose incomplete or unsupported acquisition. Use for file summaries, code explanations and configuration overviews.
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。
Research a specific technology, library, system, protocol, or tool to answer a concrete technical question with source-traceable evidence. Use when current implementation details, version behavior, configuration, compatibility, or authoritative technical facts must be verified rather than recalled. Also use when explicitly asked to incorporate verified findings into an existing skill.
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。
Route requests to summarize files, URLs, images, inline text, or multiple sources. Select the requested format and preserve evidence, exact counts, uncertainty and source coverage through summarization, synthesis and agent-result relay.
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 run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.
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 pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.
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 facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.
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 specific pytest failures using authoritative contracts and a cited product/test evidence chain. Use for failing test names or pytest output, distinguishing product, interface, oracle, and harness defects without automatically changing expectations or forcing an architectural redesign.
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 a completed plugin refactor against its task evidence, baseline assessment, documentation, and current validators. Use after implement-refactor or when deciding whether refactoring is complete.
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 and improve processes or systems using an explicit semantic model, adaptive instruction resolution, evidence-driven change, and claim-level validation. Use when reviewing process quality, resolving ambiguity or gaps, deciding how much procedural detail is necessary, or validating consequential process behavior.
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Design validation and maintained regression protection before test authoring. Use for TDD increments, acceptance or regression evidence, test boundary/oracle decisions, and multi-factor interaction coverage.
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Verify one falsifiable factual claim against evidence appropriate to the claim. Use when a concrete assertion about software, documentation, configuration, releases, or repository behavior must be classified as VERIFIED, REFUTED, or INCONCLUSIVE.
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。
Compatibility route for existing callers that supply files/scope or quality-gate evidence and need diagnosis and resolution.
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 investigating failing tests or deciding whether to change the test or product. Validate test expectations against authoritative intent, trace product and test boundaries, and require evidence for the correction rather than automatic code fixes or test dismissal.
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。
Discover and run configured quality gates for requested or changed work, preserve diagnostics as evidence, route failures to the appropriate causal/domain capability, and verify corrections. Use for linting, formatting, type-checking, configured hook checks, or pre-completion quality verification.
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。
Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.
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 existing tests against the software and contracts they actually exercise. Use to assess whether a test has justified unique protection, detects consequential faults, tolerates valid refactors, earns its lifecycle cost, or deserves strengthening, replacement, consolidation, or removal consideration. Reviews are read-only and evidence-backed, including TDD and regression tests.
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。
Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.
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。