6,640 件 ・ 関連度順
概要と使いどころ
当用户用 Claude 写小红书、公众号、视频脚本等内容时，输出出现翻译腔、英文思考、西式框架，导致“像外国人假装写中文”时使用。 典型触发语句： - “Claude写小红书翻译腔太重”“英文思考输出灾难” - “帮我把这个笔记改成纯中文网感”“去除Claude翻译腔” - “公众号文章中途变英文了”“50 Skills内容生成翻车” - “强制Claude用中文思考写笔记”“小红书情绪化表达润色” 始终保护用户原有的 CLAUDE.md / AGENTS.md 中的人设与语气要求，只做最小必要干预。优先与 xiaohongshu 技能组合使用。
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。
Options trading strategy analysis and simulation tool. Provides theoretical pricing using Black-Scholes model, Greeks calculation, strategy P/L simulation, and risk management guidance. Use when user requests options strategy analysis, covered calls, protective puts, spreads, iron condors, earnings plays, or options risk management. Includes volatility analysis, position sizing, and earnings-based strategy recommendations. Educational focus with practical trade simulation.
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。
Fix misleading Flutter/Dart test failures where expected data is empty ([]) or default values instead of the mocked response, caused by stale Mockito generated mocks or missing stubs being silently swallowed by try/catch in provider/repository code. Use when: (1) Test expects non-empty data but gets [], (2) Mock stubs for a method are set up but the code under test returns fallback/empty results, (3) A PR added new methods to a service class and tests that previously passed now fail with misleading "expected X, actual: empty" assertions, (4) "Generated files are out of date" CI error alongside test failures. The root cause is MissingStubError thrown by unstubbed mock methods, caught by production try/catch blocks, causing silent fallback to empty 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。
Run before pushing a PR that touched UI to find untranslated keys in any of the 21 non-English locales and to catch user-visible English strings that bypass context.l10n. Reports findings as a checklist; refuses to declare clean until all are addressed or explicitly waived. Invoke with /check-l10n.
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。
Fix password/secret authentication failures caused by trailing newlines when creating Google Cloud secrets (or similar) with bash here-strings. Use when: (1) Password authentication fails with correct password, (2) Secret created with `<<< "value"` syntax, (3) Error like "password authentication failed" or "invalid token" despite correct value. Bash here-strings (`<<<`) add a trailing newline that corrupts secrets.
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。
Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code 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。
Identify and report code smells indicating poor design or maintainability issues in Python code, including duplicate code, magic numbers, hardcoded values, God classes, feature envy, inappropriate intimacy, data clumps, primitive obsession, and long parameter lists. Use when conducting code quality audits, preparing for refactoring, improving codebase maintainability, or performing design reviews. Produces markdown reports with severity ratings, locations, descriptions, and specific refactoring recommendations with before/after examples. Triggers when users ask to find code smells, identify design issues, suggest refactorings, improve code quality, or detect maintainability problems.
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 efficient Blender repetition, Array-driven stairs/modules/openings, instancing, scattering, Bezier/NURBS curve-profile geometry, Geometry Nodes fields, repeat zones, simulation zones, particle-like systems, and collective motion. Use for stairs, railings, trim, pipes, cables, repeated windows/panels, vegetation, debris, crowds, rain, snow, modular structures, or any request involving dependent elements or reusable parameters.
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。
Synthesize user research findings from interviews, surveys, and analytics. Create insight reports, customer journey maps, and actionable recommendations based on research data and qualitative findings.
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 this skill whenever the user wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.md.
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。
Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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。
Read, edit, redline, comment on, and create Microsoft Word .docx files. Use to fill out or edit a Word doc, redline a contract with tracked changes, add/resolve comments, replace text keeping its formatting, restyle headings/fonts, edit tables, or read/extract a .docx as Markdown or text. Also BUILD a new .docx — from Markdown or programmatically (code that outputs a Word report with headings, tables, images). Not for PDF, Google Docs, Excel, PowerPoint, or .doc.
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。
Think and work like an expert FPGA Engineer. Use when a task calls for FPGA Engineer judgment. Reasons from metastability budgets, setup/hold margins, and tool-reported WNS/TNS through Vivado/Quartus timing and CDC reports, XDC/SDC constraints, Spyglass/Verilator lint, and ILA/IBERT lab bring-up while treating unsafe clock-domain crossings, reset domain crossings, sim-versus-silicon X mismatches, and unconstrained timing paths as first-class failure modes.
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。
Aggregate a retort run's findings.jsonl into a machine-readable assessment.json summary with severity counts, penalty score, requirement coverage, and top findings.
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。
Show WOZCODE savings report - calls saved, time saved, tokens saved, and lifetime totals.
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。
Show the WOZCODE savings report — calls saved, time saved, tokens saved, and lifetime totals.
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。
Deprecated alias — use `/woz 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。
Compare WOZCODE vs vanilla Claude Code on the user's codebase — real cost, turn, and time savings. TRIGGER on "compare woz", "how much does woz save", "benchmark woz", "woz vs claude", "show me savings", or /woz-benchmark.
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。
PEG valuation, Price Earnings to Growth ratio, Peter Lynch PEG methodology, growth-adjusted valuation, earnings growth rate, PE ratio valuation, PEG sector comparison, undervalued growth stocks, fair value PEG
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。
Recent quarter performance analysis, quarterly earnings review, last quarter results, quarterly financial performance, analyze recent quarter, Q4 earnings, quarterly revenue breakdown, EPS this quarter, margin analysis recent quarter, sequential growth, quarterly performance review
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。
Med-adapted earnings preview: consensus estimates, historical surprises, guidance sensitivities, and the FDA-catalyst overlay (PDUFA/AdCom/trial readouts near the print) for biotech/pharma names.
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。
Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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。
Deep behavioral audit of a Lattice skill — proposes 3 review personas relevant to the skill, runs independent scenario analysis from each persona's perspective, then merges only the high-confidence, practical findings into a severity-ordered gap report with proposed fixes. Structural validation (conventions, cross-references) is skill-validate's job — this skill finds gaps that would realistically surface when someone actually uses the skill: missing scenario handling, ambiguous instructions, silent failure cases, and behavioral inconsistencies. Filters out theoretical edge cases, low-likelihood speculation, and findings owned by other skills. Use after writing or significantly changing any skill, or when the user says 'review this skill', 'deep review', 'does this skill work', 'find gaps in this skill', 'stress test this skill', 'review from different angles', or 'skill review'. Standalone — does not call other skills.
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 an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.
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。