Loading...
Loading...
Found 46 Skills
Peter Thiel's Monopoly Creation framework applied to a business idea. Spawns a team of specialist agents — Monopoly Anatomist, Secret Hunter, Market Framer, Last Mover Analyst, Girardian — who each apply a distinct lens from Thiel's framework to evaluate whether a venture has genuine monopoly potential. The lead synthesizes into a verdict: does this company have a secret, a 10x advantage, a tiny domination-ready market, and a path to becoming the last mover in its category? Use when the user says "thiel this", "monopoly test", "zero to one analysis", "does this have monopoly potential", or proposes a venture and wants Thiel-style evaluation. Works standalone or after /office-hours and /munger.
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".
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Transform AI-assisted drafts into authentic, human-sounding content. This skill provides patterns to detect and eliminate AI tells, frameworks for natural writing, and techniques for creating prose that reads as genuinely human. Use when reviewing any AI-generated content or when writing content that must not appear AI-assisted.
Software architecture and UI/UX principles for building genuinely new solutions, not derivative work. Use when designing features, architecting software, brainstorming apps, reviewing designs, or during strategy discussions. Focuses on first-principles thinking, simplicity where it matters, and creating rather than commenting.
Use when the user wants text to sound more human, says writing sounds "too AI" or "too ChatGPT," asks to humanize or rewrite a draft to feel natural, or shares content wanting it to feel authentic and less robotic. Also applies to LinkedIn posts, blog drafts, or emails where the user wants a more genuine voice.
When the user wants to improve their ability to create genuine connection and trust quickly with prospects. Also use when the user mentions "connecting with prospects," "building trust," "relationship selling," "warming up cold leads," "getting prospects to open up," or "first impressions."
Root-cause-driven solution decision framework for the hardest problems across any domain. This is the nuclear option — it consumes significant tokens through exhaustive multi-branch root cause analysis, MECE solution enumeration, and domain-adaptive external validation. Use ONLY for genuinely difficult problems: recurring failures that resist repeated fix attempts, complex systemic issues with no clear solution path, decisions where multiple approaches exist and the wrong choice has high cost, problems with multiple interacting causes spanning components or teams. Trigger when: the user says 'what's the best way to fix X', 'why does this keep happening', 'how should we approach this', 'find the root cause', 'what are my options for fixing X', 'analyze this problem systematically', 'evaluate our options for X', 'what's the right approach and why', or expresses frustration that previous solutions didn't stick. Do NOT use for: problems where the answer is already obvious or requires no analysis, straightforward issues with clear solutions, or routine investigation. If the problem can be solved in 5 minutes of investigation, this skill is overkill.
Run a decision through 5 AI advisors with different thinking styles, anonymous peer review, and chairman synthesis. For genuine decisions with stakes and tradeoffs — not simple questions. Based on Karpathy's LLM Council.
Generates genuinely novel, useful ideas for products, businesses, features, campaigns, names, research directions, and process redesign. Use when the user asks to brainstorm, ideate, improve a weak concept, escape generic answers, find differentiated options under real constraints, or turn a vague opportunity into a shortlist of strong concepts with wedges and tests. Preserves diversity with independent idea pools, analogy transfer, contradiction solving, critique-and-repair, and reality checks. Do not use for simple rewriting, proofreading, or purely factual research.