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Found 203 Skills
Run fact-grounded image generation batches for short-form video production, especially persona images, first-frame candidates, and light consistency edits. Use this when persona and concept inputs already exist and you need local image assets, prompt records, and reusable model-call metadata. This skill should stay anchored to benchmark-backed persona locks and should save both raw provider responses and normalized local asset manifests.
Interactively prune stale non-terminal workflows from the pipeline. Use when the user says 'prune workflows', 'clean stale workflows', 'pipeline cleanup', or runs /prune. Runs a dry-run preview, displays candidates with staleness and safeguard skips, prompts the user to proceed/abort/force, then bulk-cancels approved workflows with a workflow.pruned audit event. Safeguards skip workflows with open PRs or recent commits unless force is set.
Graham cigar-butt batch screener — runs Benjamin Graham's NCAV / net-net / defensive-investor hard filters across an index or market universe and returns a ranked candidate list with NCAV ratio, PE, PB, dividend yield, debt coverage, 5y earnings stability, Graham buy price, and a dynamic value-trap warning. Longbridge CLI/MCP first; WebSearch fills genuine gaps (PMI, sector outlook). Every figure footnoted to its source. Auto-switches model for banks / insurance / REITs and flags <2y IPOs and suspended names. Triggers: "格雷厄姆筛选", "格雷厄姆选股", "捡烟蒂榜单", "烟蒂股榜", "NCAV筛选", "NCAV排行榜", "净流动资产筛选", "防御型投资者选股", "撿煙蒂榜單", "煙蒂股榜", "NCAV篩選", "淨流動資產篩選", "防禦型投資者選股", "Graham screen", "Graham screener", "NCAV screen", "net-net screen", "net-net list", "cigar-butt screen", "defensive investor screen", "liquidation value screen", "Benjamin Graham screen".
Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.
Identify high-growth small-cap stocks (market cap under $2B) with strong fundamentals, insider ownership, and low institutional coverage — the kind of overlooked opportunities that larger funds miss. Use when the user asks to find small-cap growth stocks, discover under-the-radar companies, screen for founder-led small caps, identify emerging growth companies, find low-institutional-ownership stocks, or seek multi-bagger candidates with strong fundamentals.
Track which stacks use a specific Pulumi package and at what versions, or upgrade a stack to the latest version of a package. Use when users ask about package version tracking, outdated package versions across stacks, upgrade candidates, or package usage audits. Also use when users want to upgrade/update a specific package version in a stack or project. Do NOT use for general infrastructure creation, resource provisioning, or questions about how to use a package.
Apply Porter's Value Chain Analysis to identify competitive advantage sources within an organization's activities. Use this skill when the user needs to find where value is created or lost in their operations, analyze cost structure by activity, optimize internal processes, or identify outsourcing candidates — even if they say 'where do we make money' or 'which activities should we keep in-house'.
Fundamental factor stock screening — filter value or growth stocks using PE, PB, ROE, revenue growth, net-profit growth, and dividend yield across A-share, HK, and US markets. Outputs a candidate table ranked by composite factor score. Triggers: "基本面筛选", "因子选股", "价值选股", "成长选股", "低PE选股", "高ROE", "股息筛选", "PE筛选", "PB筛选", "多条件选股", "基本面因子", "量化选股", "基本面篩選", "因子選股", "價值選股", "成長選股", "低PE選股", "股息篩選", "factor screening", "value screen", "growth screen", "low PE filter", "high ROE screen", "dividend screen", "fundamental factor", "multi-factor stock screen".
Investment idea generation — systematically surfaces new investment opportunities by combining quantitative screening (low valuation / high momentum / improving fundamentals), thematic research (sector trends / policy catalysts), and pattern recognition (historical analogues), producing a long/short candidate list. Triggers: "投资想法", "选股灵感", "投资机会", "找股票", "发掘机会", "多头机会", "空头机会", "主题投资", "投資想法", "選股靈感", "投資機會", "找股票", "多頭機會", "空頭機會", "主題投資", "investment ideas", "stock ideas", "investment opportunities", "idea generation", "long ideas", "short ideas", "thematic investing", "stock discovery", "find me stocks", "what should I buy".
Suggests code simplification opportunities. Identifies extract method candidates, complex expressions, redundant code, refactoring opportunities.
Batch identify candidate stocks with mature breakout patterns, healthy volume-price structures, and good catalyst alignment, and output priorities, trigger conditions, and failure boundaries. Suitable for scenarios such as short-to-medium-term stock selection, pre-market candidate pool sorting, and screening leading candidate stocks in sector rotation.
Use this when the user wants to post a daily X/Twitter tweet inspired by one of their recently published WeChat Official Account articles. It selects the newest article that hasn't been tweeted yet, drafts 3 tweet candidates from it (from different angles — quote / metaphor / one-liner), posts the selected one via xurl, and records the action to history. Triggers — "Post a daily tweet", "Tweet from an article", "Today's tweet", "/wjs-tweeting-from-articles".