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Found 1,907 Skills
MaxIQ platform help — AI-native revenue intelligence with EchoIQ conversation intelligence, InspectIQ pipeline visibility, ForecastIQ AI-driven forecasting, 9 AI agents (NoteTaker, Radar, Summarizer, Coach, Taskmaster, Watchdog, Forecaster, Revenue Planner, Deal Mapper), usage-based pricing (no per-seat), Salesforce/HubSpot CRM sync. Use when EchoIQ not capturing all meeting types, AI Coach scoring criteria not matching your sales process, CRM fields not auto-populating from calls, InspectIQ deal signals seem inaccurate, ForecastIQ predictions not matching reality, comparing MaxIQ vs Gong vs Clari for revenue intelligence, setting up AI Radar keyword tracking, or evaluating usage-based CI pricing vs per-seat alternatives. Do NOT use for designing outbound cadences (use /sales-cadence) or cross-platform coaching programs (use /sales-coaching).
BrandJet AI platform help — multi-channel outreach sequences, unified inbox, brand monitoring, AI visibility tracking, lead discovery, social listening, email warmup, Artemis AI agent, and integrations. Use when outreach sequences aren't getting replies, brand mentions going unnoticed, multi-channel sequences feel disjointed, unified inbox is overwhelming, or AI visibility scores are dropping. Do NOT use for designing cadence strategy (use /sales-cadence), cross-platform deliverability (use /sales-deliverability), social listening strategy (use /sales-social-listening), or enriching contacts (use /sales-enrich).
Wind MCP Data Bridge Skill (v1.1.0, 6 servers / 19 tools). Route by `server_type`: (1) `quote` for market data (A-shares/Hong Kong stocks snapshots, daily/weekly/monthly K-lines, minute-level data); (2) `fund_data` for fund-related data (profile/finances/holdings/performance/holders/management company); (3) `stock_data` for in-depth stock data (profile/financial fundamentals/equity structure/events/technical indicators/risk); (4) `financial_docs` for document RAG (announcements/financial news); (5) `economic_data` for EDB macro + industry economic indicators; (6) `analytics_data` for general NL → Wind data. WIND_API_KEY is required (obtained by logging into the Developer Center at aimarket.wind.com.cn). Trigger scenarios: A-shares/Hong Kong stock codes/K-lines/minute-level data, any dimension of funds, stock financial reports/valuation, listed company announcements/financial news, macroeconomic data, cross-comparison of targets. **Excluded**: US stocks/European stocks/Japanese stocks, exchange rates/futures quotes, cryptocurrencies, non-financial data.
End-to-end testing patterns with Playwright for full-stack Python/React applications. Use when writing E2E tests for complete user workflows (login, CRUD, navigation), critical path regression tests, or cross-browser validation. Covers test structure, page object model, selector strategy (data-testid > role > label), wait strategies, auth state reuse, test data management, and CI integration. Does NOT cover unit tests or component tests (use pytest-patterns or react-testing-patterns).
Read-only observability dashboard plugin for Hermes Agent — journeys, crossings, guideposts, and reports.
Audit a python-pptx export against its source HTML deck, identify layout/content drift (footer overflow, cropped content, missing italic/em, lost styling, off-rhythm spacing), and re-export with strict footer-rail + cursor-flow layout discipline. Use this skill whenever the user has a .pptx that was generated from an HTML slide deck and asks to compare/audit/verify/fix the export — including phrases like "compare ppt with html", "fidelity audit", "fix the pptx", "ppt is cut off", "footer overlap", "italic missing in pptx", "re-export the deck", "pptx-html-fidelity-audit", or any case where a python-pptx → HTML round-trip needs verification or repair. Also trigger when the user shows you a deck.html and a deck.pptx side by side and is debugging visual differences.
NCAA cross country and track & field athlete data via TFRRS (tfrrs.org) and news via The Stride Report. Fetch athlete profiles including all personal records (PRs), eligibility year, school, full season-by-season results history, and XC/TF news. Zero config, no API keys. Use when: user asks about NCAA cross country, NCAA track and field, college running, TFRRS athlete profiles, personal records, PRs, XC or TF season results, individual athlete performance history, or XC/TF news. Don't use when: user asks about professional track, Diamond League, or other sports — use nfl-data, nba-data, wnba-data, nhl-data, mlb-data, golf-data, cfb-data, cbb-data, tennis-data, fastf1, or volleyball-data. For betting use polymarket or kalshi.
Plan a non-trivial code change end-to-end — size triage (XS–XL), slicing strategy, optional parallel subagent dispatch, per-slice Implement → Test → Verify → Commit discipline. Use for any multi-file change, refactor across files, executing a planned task from any planning source, cross-cutting modification (analytics sweep / i18n / library migration), or when about to write more than ~100 lines. 也用于增量实现 / 切片落地 / 推进已规划任务 / 跨切面改动。Skip only for trivial XS edits and pure documentation / configuration changes.
Hedging strategy design framework — Beta hedge ratio (portfolio vs benchmark), option protection strategies (protective put / collar), tail-risk hedges (VIX-related / gold / treasuries), cross-asset hedges (currency risk), and hedge cost assessment (option premium vs protection value). Triggers: "对冲", "对冲策略", "Beta对冲", "保护性看跌", "领口策略", "尾部风险", "汇率对冲", "对冲比率", "對冲", "對冲策略", "Beta對冲", "保護性看跌", "領口策略", "尾部風險", "hedging", "hedge strategy", "beta hedge", "protective put", "collar strategy", "tail risk hedge", "currency hedge", "hedge ratio", "portfolio insurance".
Competitive landscape analysis — builds a competitive structure research framework covering market positioning (Porter five-forces), peer cross-comparison (PE/PB/ROE/revenue growth), market share estimation, competitive advantage assessment (moat), and potential disruptor identification. Triggers: "竞争格局", "竞争分析", "行业竞争", "市场份额", "竞争对手", "护城河", "波特五力", "竞争优势", "競爭格局", "競爭分析", "行業競爭", "市場份額", "競爭對手", "護城河", "波特五力", "competitive analysis", "competitive landscape", "market share", "competitive moat", "Porter five forces", "industry competition", "competitive advantage", "market positioning", "moat analysis", "NVDA vs AMD", "who are the competitors".
Generate a high-converting ad creative set — hero image, ad copy variations, and platform-optimized crops for Meta, Google Display, and LinkedIn.
Generate a full multi-channel product campaign — hero visuals, social media assets, short ad video, and platform-specific crops for an end-to-end launch campaign.