“structured generation”的搜索结果

Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.

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Convert requirements into structured technical specifications with architecture decisions

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Specification creation and management for the Pilot Shell methodology. Covers semantic search, clarifying questions, structured spec generation, and iterative refinement.

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Generate, visually expand, optimize, diagnose, and route structured AI image prompts for adult female portraits. Use for lifestyle, curve-focused, fashion, oriental, fantasy, realism, beauty, CCD, low-key cinematic, studio, sport, travel, and e-commerce portrait requests; onboarding or usage help; parameter recommendations; prompt rewrites; image-to-prompt preparation; direct image generation; or identity- and product-preserving edits from authorized reference images. Preserve explicit user parameters and locked reference subjects while creating a coherent photography-directed scene.

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Generate structured research questions, testable hypotheses, and candidate empirical strategies from a topic, phenomenon, or dataset description. Use when user says "give me research ideas on X", "brainstorm questions about Y", "what could I study with this data?", "I'm looking for a paper idea on...", "generate hypotheses for...". One-shot generation, not multi-turn. For idea-refinement use `/interview-me`.

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Generate 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Tests can run via LinkedIn organic posts, cold email subject line splits, or both. Pure reasoning for variant generation and analysis — the user deploys the tests through their own tools. Use when a team can't decide between messaging angles and needs data, not opinions.

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Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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Structured peer review analysis and feedback generation

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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

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Structured divergent thinking techniques — HMW framing, SCAMPER, Crazy 8s mechanics, and option diversity guarantees. Enforces strict separation of generation and evaluation phases.

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Generate, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.

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Structured plan file generation for large tasks -- phased planning, completion tracking, and decision documentation

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AI art direction system. Claude directs image generation models (Gemini, DALL-E, Flux) via structured prompts. Generate banners, diagrams, logos, screenshots, and social media visuals without leaving the terminal.

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Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.

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Use when you want to build a project but don't have a spec yet and need to brainstorm the idea into a design doc structured for pipeline DOT generation. Use when starting from a vague idea, project concept, or feature request that needs to become a headless autonomous build pipeline.

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Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and verification.

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Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.

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Protocol for handling ambiguous decisions and missing/conflicting knowledge during code generation, design, and review. Ensures AI surfaces genuine judgment calls with structured options and stops on hallucination risk instead of silently assuming. Use when a decision has multiple valid approaches, when facts are missing or contradictory, when the user asks 'what should we do here?', 'is this a judgment call?', 'should I ask about this?', 'am I guessing here?', 'what are the tradeoffs?', or when deciding between two reasonable architectural or design options. Also composed by molecules to define how judgment calls and clarification requests are surfaced and resolved.

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Generates a fully structured SEO content **outline** (not a finished brief) and exports it as a formatted .docx Word document. The output is a skeleton for a writer to fill in — section headings, topic prompts, angles — not pre-written paragraphs. Use this skill whenever a user provides a blog title, focus keyword, domain URL, or any combination of those and asks to generate an outline, brief, blog brief, SEO brief, article structure, or content plan. Also trigger when the user says "create a brief for X", "generate an outline for [topic]", "make a content brief", "I need a brief for [URL or keyword]", or pastes a title and asks what the structure should look like. This skill handles input validation, domain analysis, keyword enrichment, audience inference, archetype-aware section selection, and full .docx generation — always use it rather than writing ad-hoc outlines.

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Parse git history and produce or update a CHANGELOG.md following the Keep a Changelog convention. Supports Conventional Commits, basic prefix conventions, and unstructured commit messages. Intelligently categorizes changes, detects breaking changes, links to PRs/issues, and handles both initial generation and incremental updates. **Triggers — use this skill when:** - User asks to "generate", "create", "update", or "write" a changelog - User mentions "CHANGELOG", "changelog", "release notes" - User says "document changes", "what changed since last release" - User wants to "prepare a release" and needs a changelog entry - User asks to "clean up" or "reformat" an existing changelog **Covers:** Any git-based project. Handles Conventional Commits (feat/fix/chore), Angular convention, basic prefixes (Add/Fix/Remove), and freeform commit messages. Outputs Keep a Changelog format with optional Common Changelog enhancements.

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Directly generate design-oriented AI images with strong creative direction and prompt engineering. Use this skill for posters, product visuals, PPT illustrations, infographics, teaching/demo diagrams, campaign key visuals, cover art, or when the user wants design-quality image generation rather than generic AI art. This skill turns a loose brief into a design brief, assembles a structured prompt, routes to the right Volcengine Seedream settings, and can generate the image immediately.

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Structured idea generation + multi-LLM debate for the product-owner stage. Diverge (generate genuinely different bets), debate (a 4-persona panel on 4 models argues over 2 rounds), converge (synthesize a recommendation). Used by product-owner before architect; available to architect for design-space exploration.

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