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Found 578 Skills
Qualifies inbound leads against full ICP criteria — company size, industry, use case fit, role/seniority of the person. Checks CRM and existing customer base for duplicates and existing relationships. Outputs a scored CSV with qualification status, reasoning, and pipeline overlap flags. Tool-agnostic — works with any CRM, enrichment tool, or data source.
Write high-converting cold emails using structured frameworks, personalization tiers, and patterns from real campaigns. Pure reasoning skill — no scripts. Auto-loads when any task requires outreach copy.
Reset Claude Code buddy/companion by changing the season seed (fk_) in cli.js and removing the companion field from ~/.claude.json, allowing re-hatching a new buddy. Triggers: buddy reset, reroll buddy, new buddy, change buddy, reset companion, reroll companion, buddy reroll
Assess investment suitability obligations under FINRA Rules 2111 and 2090 across all three suitability prongs. Use when the user asks about reasonable-basis, customer-specific, or quantitative suitability, product-specific concerns for complex products, leveraged ETFs, variable annuities, or alternatives, household-level suitability, hold recommendations, or the institutional suitability exemption. Also trigger when users mention 'is this investment suitable', 'turnover ratio is too high', 'cost-to-equity ratio', 'churning metrics', 'suitability questionnaire design', 'complex product due diligence', 'customer refused to provide their risk tolerance', or ask whether a recommendation fits a customer's profile.
Expertise in F2P economics, virtual currencies, and ethical monetization strategiesUse when "game monetization, F2P economy, in-app purchase, IAP strategy, battle pass design, loot box, gacha system, virtual currency, player LTV, whale monetization, game economy balance, premium currency, season pass, daily rewards, pay to win, ethical monetization, monetization, f2p, free-to-play, iap, in-app-purchase, battle-pass, season-pass, gacha, loot-box, virtual-economy, game-economy, ltv, arpu, retention, whales, pricing, microtransactions" mentioned.
Intent-Augmented Code Property Graph — tracks WHY code exists via ReasonNodes with formal contracts, 6-dimension drift detection, and 3 canonical pre-task queries for autonomous development
Solve the newsvendor problem for single-period ordering decisions under uncertain demand. Use this skill when the user needs to determine optimal order quantity for perishable goods, seasonal products, or one-time purchase decisions — even if they say 'how much to order for this season', 'perishable inventory', or 'single-period ordering'.
Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.
Information Question Generator. Given an article, paper, or book, extract its core viewpoints into Q-A pairs — Questions get straight to the point, no textbook-style phrasing; Answers are concise and clear, with formalized conclusions and complete logical chains. As readers follow the Q chain, each Answer drives home a key point, reproducing the author's entire reasoning process. Activate when the user says '问答', 'Q&A', 'QA', '提问', '抽取问题', '/ljg-qa', or shares an article, paper, or book and requests Q-A extraction. This tool triggers when the user wants ideas extracted not as a summary but as a sequence of incisive questions paired with answers. NOT FOR FAQ generation, glossary creation, or comprehension quizzes — this is intellectual scaffolding, not a study aid.
INTERNAL sub-agent for blind 9-dimensional rubric scoring. **NOT a user-facing skill — do NOT invoke from the main conversation.** It is called via the Task tool by cheat-score / cheat-predict / cheat-bump to generate a context-isolated score for a script. It ONLY accepts script_path + rubric_notes_path; any other input will be refused. It outputs strict JSON: 9 dimensions × {score 0-5, confidence enum, one-line reason}. **It strictly refuses to read** .cheat-state.json, predictions/*, retro sections, or any content that may leak post-publish data. This is Channel B in the 3-channel calibration model (A=main, B=blind sub-agent, C=cross-model).
Use when the user asks for repeated rollouts, marked decision processes, high-dimensional search, stochastic optimization, local-optima exploration, ensemble comparison, or recursive reasoning with a visible evidence trail.
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.