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Found 594 Skills
Primarily the agent's internal-thinking skill — invoke it silently to model a problem, identify trade-offs, and decide what to do, BEFORE asking the user anything or dispatching another skill. Workflow skills call `/culture` as their step-1 reasoning pass; the agent does not surface the dialogue. Only treat this as a user-facing skill when the user has explicitly opted out of writes — phrases like "no writes", "just rubber-duck this", "let's only talk", "/culture". In the user-facing path the output is conversation; the only sanctioned artifact is an opt-in `.cheese/notes/<slug>.md` handoff slug at session end if the user asks for notes. Culture never writes to production code, never commits, never opens PRs. If the dialogue reveals real work, recommend `/mold` (fuzzy → spec) or `/cook` (clear ask → code) and stop. Before `/mold` or `/cook`.
Recover a suppressed, blocked, or deactivated Amazon listing. Maps the suppression reason code to the specific fix path and produces the reinstatement message. Use when a user asks about a suppressed listing, a blocked listing, listing deactivated, pricing error suppression, image policy, restricted phrase, or missing required field. Trigger phrases: "suppressed listing", "blocked listing", "listing deactivated", "pricing error", "image suppression", "restricted phrase". Works with zero tools. the user pastes the suppression notice.
Diagnose why an Amazon product gets returned and build a plan to cut the return rate. Clusters returns into five named root causes, quantifies the dollar bleed per return including the Returns Processing Fee where it applies, and fixes each cause at the listing, the product, or the packaging. Use when a user asks about reducing returns, a high return rate, why customers return a product, return reasons, the Returns Processing Fee, or refunds eating profit. Trigger phrases: "reduce returns", "return rate", "why are customers returning", "return reasons", "too many refunds", "cut returns", "Returns Processing Fee", "returns eating margin". Works with zero tools. the user pastes return reasons and reviews.
Systematic debugging that identifies root causes rather than treating symptoms. Uses sequential thinking for complex analysis, web search for research, and structured investigation to avoid circular reasoning and whack-a-mole fixes.
Expert guidance for writing Python code using the official Google GenAI SDK (google-genai) for Gemini API and Vertex AI. Use for text generation, multimodal inputs, reasoning, tools, and media generation.
Create professional SVG graphics powered by Gemini 3.1 Pro via the Gemini MCP server. Generates logos, icons, illustrations, infographics, patterns, animated SVGs, and UI elements with a dual-model refinement loop (Claude orchestrates + Gemini generates). Gemini 3.1 Pro has SOTA animated SVG capabilities and advanced reasoning. Use this skill when the user asks to: create an SVG, design a logo, make an icon, draw an illustration, create an infographic, design a pattern, make an animated SVG, generate vector graphics, create SVG art, or any request involving SVG creation or generation. Also triggers on: 'generate SVG', 'draw me', 'design graphic', 'create vector', 'SVG illustration', 'SVG icon', 'SVG animation', 'create badge', 'design emblem', 'make a diagram'.
Respond to PR review feedback with maximum transparency. Analyze feedback, get second opinions on non-trivial decisions, post comments documenting every judgment, and implement fixes. All reasoning must be visible in PR comments. Keywords: PR review, code review response, review feedback, address comments, fix review.
Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
Programmatic JDBC in Quarkus with Agroal DataSource, parameterized SQL, transactions, batching, and Dev Services. Part of the skills-for-java project
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
Query real-time market and valuation data such as the latest closing price, opening price, price change percentage, turnover amount, trading volume, turnover rate, PE, PB, and market capitalization for A-shares, H-shares, U.S. stocks, and their indices. Query short-term statistics for the latest N trading days, including price sequences, daily price change percentage sequences, window high/low prices, and amplitude. Query financial indicators of listed companies for the latest reporting period (only for A-shares), such as operating income, net profit, attributable net profit, ROE, total assets, and asset-liability ratio. Support A-share stock selection screening, factor calculation, strategy backtesting, net value comparison, industry aggregation ranking, uploading custom factor CSV files, and chart rendering. Currently, H-shares and U.S. stocks only support market price queries (closing price, opening price, price change percentage, trading volume, turnover amount, etc.). Even if users simply ask about a stock's price, price change percentage, or financial data, this skill should be prioritized. Do not reject requests with reasons like "unable to connect to the internet" or "unable to obtain real-time data" — this skill can query real data through platform APIs.
Mine Amazon reviews for product, listing, and marketing intelligence. Extracts recurring complaints, feature requests, the words customers use, and the reasons they chose the product, then turns them into listing fixes, product improvements, and copy. Use when a user pastes reviews and asks to analyze them, find complaint patterns, mine reviews, understand customer sentiment, pull insights from competitor reviews, or turn reviews into improvements. Trigger phrases: "analyze reviews", "review analysis", "complaint patterns", "what customers say", "mine reviews", "competitor reviews". Works with zero tools. the user pastes the reviews.