'structured output' 검색 결과

Review prose in Quarto manuscript files for clutter, weak verbs, inconsistent voice, and unclear explanations using William Zinsser’s writing principles. Receive a separate Markdown report with section-based recommendations and reasons, while the source file remains unchanged.

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• Finding wordy expressions in an academic manuscript
• Reviewing only the introduction
• Checking section openings and conclusions

Review Quarto manuscripts against APSA style rules for political science. Get a separate report grouped by document section, with suggested changes and explanations for numbers, capitalization, citations, terminology, and neutral language, while preserving the source.

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• Checking a political science manuscript against APSA style
• Reviewing citation wording and number formatting
• Flagging potentially biased language

Review economic reports for methodological gaps, unsupported figures, and errors such as double counting. Produce a red, amber, and green scorecard with issues ranked by severity, using the relevant appraisal framework and proposing corrections when requested.

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• Checking fiscal and macroeconomic briefings before submission
• Reviewing cost-benefit assumptions and double counting
• Auditing reports against a selected appraisal framework

Turn a business scenario into a compliance checklist covering GDPR and Chinese privacy, data, and advertising regulations. Organize legal references, risk levels, current status, and recommended actions into a prioritized remediation plan.

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• Preparing a compliance checklist before launching a feature
• Reviewing user profiling and personalized recommendations
• Organizing cross-border data transfer checks

Inspect and manage BB through its command-line interface. Find the relevant commands for threads, projects, environments, models, and plugins, then check actual status and results while handling identifiers, file paths, and structured output appropriately.

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• Inspect thread status and logs
• Check project and execution environment settings
• Find available models and plugins

Turn a rough request into a structured AI prompt with a clear goal, output format, and constraints. For complex work, add smaller tasks, separate creator and reviewer roles, and explicit checks for accuracy and uncertainty.

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• Refining a vague AI request
• Designing prompts for API documentation
• Adding verification steps to code generation

Use the gh command-line tool to manage GitHub issues and pull requests, check CI automated checks, and inspect workflow failures. Retrieve failed-step logs or query the GitHub API for specific information in structured output.

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• Creating pull requests
• Listing repository issues
• Investigating failed CI checks and logs

Use btca ask to inspect reference SDKs, libraries for connecting to services, when building features or debugging provider-specific issues. Compare streaming, tool calling, structured output, and validation approaches in Vercel AI SDK, Anthropic SDK, and Zod.

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• Inspect similar features before implementation
• Investigate provider-specific bugs
• Compare SDK implementations across providers

Extract writing feedback from GitHub pull request reviews and compare original wording with suggested revisions. Analyze first and final drafts to identify recurring lessons about clarity, grammar, tone, and structure for future writing.

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• Review suggested wording changes in a GitHub pull request
• Connect reviewer comments to revisions in the final draft
• Compare first and final drafts paragraph by paragraph

Organize manuscript text, figures, and references with version control and repeatable builds. Track how each figure was produced, separate source files from generated outputs, and use automated checks to prepare submission artifacts.

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• Organizing files for collaborative manuscript writing
• Tracking changes across submissions and revisions
• Reproducing figures from code, data, and fixed parameters

Turn raw test results into readable reports with pass and fail totals, unstable tests, and coverage, meaning how much code tests exercise. Package results for releases, quality assurance, or reviews, with follow-up actions.

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• Preparing test reports for a release
• Turning pytest or JUnit output into a readable summary
• Sharing coverage and quality-gate status

Fine-tune NV-Generate-CTMR MR-Brain v1 using supplied brain MRI volumes. Check the input file list, prepare training configurations, run training, and optionally generate sample volumes, producing a saved model and a structured run summary.

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• Check a brain MRI dataset before GPU training
• Fine-tune a model with T1, T2, FLAIR, SWI, or MRA data
• Prepare a training command with explicit data and output paths

Build English clinical speech-recognition benchmarks by selecting medical terms, checking pronunciations, and generating synthetic audio. Outputs include audio files and a NeMo manifest, a structured file listing recordings and metadata, ready for a separate scoring stage.

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• Building a speech-recognition benchmark for a clinical specialty
• Checking synthesized pronunciations of drug and procedure names
• Generating evaluation audio across voices, noise, and contexts

Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.

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Plan, write, and edit professional financial reports; generate finance chart configurations. Use when condensing finance analysis into a structured output.

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Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.

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Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Not for verifying statistics (blog-factcheck) or gathering authority sources. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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Operate websites and Electron apps through CLI commands, reusing Chrome login sessions. Prefer over browser_task for supported sites (GitHub, Bilibili, Twitter/X, YouTube, etc.) — deterministic commands with structured JSON output.

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Web data extraction using 55+ Apify Actors for AI-driven scraping. Supports Instagram, Facebook, TikTok, YouTube, Google, and more. Auto-selects best Actor for the task. Structured output in JSON/CSV with rate limiting and ethical scraping guidelines.

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Analyze data with `forest-plot-styler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

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Use gene structure mapper for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use facs gating viz style for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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Use lab budget forecaster for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

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