Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
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
Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
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。
Market salary research, accomplishment quantification, negotiation scripts, total compensation analysis, timing strategy.
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。
Monitor buyer intent signals across the web including job postings, tech changes, funding rounds, and leadership changes. Alerts when prospects show buying signals and prioritizes "hot" accounts. Use for timing-based prospecting.
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。
Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.
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 personalized cold email sequences (7-14 emails) with A/B test subject lines, follow-up timing recommendations, and integrated social proof. Creates multi-touch campaigns optimized for response rates. Use when users need outbound email campaigns, sales sequences, or lead generation emails.
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。
HTTP request smuggling / desync hunting - CL.TE, TE.CL, TE.TE, CL.0, and HTTP/2 downgrade. Timing-based detection, differential confirmation, no-blind-claims. Wiki-first, FIND schema output.
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。
Scene composition and orchestration skill for creating complex multi-part animations. **Triggers when:** - User wants to compose multi-element scenes - Request involves combining multiple visual elements - Scene requires act-based structure or transitions - User mentions "scene", "compose", "combine", "orchestrate" **Capabilities:** - Scene graph construction with acts and transitions - Multi-object coordination and timing - Camera movements and focus control - Layered animation sequences
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。
Provides GDPR Article 14 information for personal data obtained from sources other than the data subject, covering timing requirements (within reasonable period, max one month), source disclosure, all required elements, and exemptions under Art. 14(5). Activate for Art. 14, indirect collection, third-party data source, indirect data notice queries.
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。
Provides GDPR Article 13 information at the point of direct data collection, covering all required elements under Art. 13(1)(a)-(f) and Art. 13(2)(a)-(g), layered notice design, and timing requirements. Activate for Art. 13, direct collection notice, privacy notice at collection, data collection information queries.
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。
Executes breach notification under California Civil Code Section 1798.82 (California data breach notification law). Covers data elements triggering notification, timing requirements (most expedient time possible), AG notification for 500+ California residents, specific content and format requirements, and substitute notice provisions. Keywords: California, breach notification, Cal. Civ. Code 1798.82, attorney general, CCPA, data elements.
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。
Enhance a finished PPTX while keeping visible content and layout stable. Use for adding or replacing speaker notes, narration audio, auto-advance timing, or slide transitions through direct OOXML. Do not use for redesign, raw template filling, SVG-generated project narration, or changing slide content.
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。
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
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。
Translate video subtitles (SRT, VTT, TXT) between languages. Supports batch translation and maintains timing synchronization. Use when creating multilingual subtitles, expanding to international audiences, or translating video transcripts.
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 retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
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 to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.
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。
Define go-to-market strategy including launch plan, messaging, channels, and timing during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "messaging", "launch channels", "GTM". Outputs GTM- entries with launch plan components.
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 personalized cold email sequences (7-14 emails) with A/B test subject lines, follow-up timing recommendations, and integrated social proof. Creates multi-touch campaigns optimized for response rates. Use when users need outbound email campaigns, sales sequences, or lead generation emails.
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 Wayback Machine CDX API returning empty results or timing out from cloud/datacenter IPs (Cloud Run, AWS Lambda, GCE, etc.) while working fine locally. Use when: (1) CDX queries timeout or return 0 bytes from Cloud Run/cloud functions but work from local machine, (2) urllib.request.urlopen times out for web.archive.org from server, (3) Wayback Machine CDX silently returns empty from datacenter IPs with no HTTP error, (4) Need to run CDX-dependent scripts on Cloud Run or similar cloud compute. Workaround: fetch locally, upload to cloud storage, import on cloud compute.
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。
Debug Fastly Compute deployments that appear successful but return stale/wrong responses. Use when: (1) fastly compute publish succeeds but version check shows old version, (2) new endpoints return 404 after deployment, (3) cache-busted requests work but regular requests fail, (4) fastly domain list doesn't show your custom domain. Covers edge propagation timing, cached error responses, and domain management API differences.
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。
Instruments programs to record execution information for deterministic replay debugging. Use when debugging hard-to-reproduce bugs (race conditions, timing issues, intermittent failures, heisenbugs), reproducing production failures, or analyzing complex execution sequences. Records non-deterministic events (I/O, threading, randomness, time) to enable exact replay of program executions. Supports Python, JavaScript, Java, and C/C++ with both custom instrumentation and existing replay tools.
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。
Check behavioral consistency between high-level hardware specifications and RTL implementations. Use when asked to check RTL consistency, verify RTL against spec, check hardware specification compliance, validate RTL implementation, find spec violations in RTL, check behavioral consistency, or when working with hardware designs that need verification against protocol specifications, timing requirements, or functional specifications in Verilog, VHDL, or SystemVerilog.
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。
Selectively instruments code to capture runtime data for debugging failures and bugs. Use when investigating crashes, exceptions, unexpected behavior, test failures, or performance issues. Analyzes stack traces and error messages to identify suspicious code regions, then adds targeted logging, tracing, and assertions to capture variable values, execution paths, timing, and conditional branches. Supports Python, JavaScript/TypeScript, Java, and C/C++.
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。
Learn reusable 2D motion-generation knowledge from user-specified action resources with `/img2mo-learn <resource>`. Use when the user provides videos, extracted frame sequences, spritesheets, Spine assets, generated outputs, failed attempts, or reference motion folders and wants to summarize animation timing, pose beats, style traits, prompt patterns, extraction/cropping rules, or failure lessons into the project `img2mo-knowledge/` folder for later `/img2motion` generation.
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 multi-touch outbound email sequences with personalized messaging, timing cadences, and conversion-aware copy for prospecting or re-engagement. Use when the user needs a coordinated sales campaign across several touches; use email-drafting for a single message or reply.
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。