'survey' 검색 결과

Use when designing, configuring, distributing, or troubleshooting Salesforce Surveys and Feedback Management. Triggers: 'create a survey', 'NPS score', 'survey invitation', 'survey response report', 'customer feedback form', 'guest user survey access', 'SurveyInvitation', 'SurveySubject', 'SurveySettings', 'sendSurveyInvitation', 'survey not linked to case', 'deploy a survey between orgs'. NOT for choosing between CSAT, NPS, and CES as your CX metric — use architect/customer-effort-scoring. NOT for third-party survey tools like SurveyMonkey or Qualtrics.

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Complete the PUA survey through a guided flow in Codex CLI and save responses to a local JSON file. This alias provides the equivalent of Claude Code’s /pua:survey command without sending answers anywhere.

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• Complete the PUA survey in Codex CLI
• Save survey answers to a local JSON file
• Use the equivalent of /pua:survey from Codex

Turn interviews, surveys, support conversations, and public reviews into evidence-based customer insights. Identify recurring problems, buying triggers, and customer language, then build research reports, quote collections, or personas and plan interviews or surveys to fill gaps.

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• Analyze customer interview transcripts
• Find recurring themes in reviews and support tickets
• Build customer personas from research evidence

Analyze customer recommendation and satisfaction surveys, NPS and CSAT, from Google Sheets. Group comments into themes, explain weekly score changes and low-score drivers, and post a Slack digest with representative quotes without modifying survey data.

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• Summarizing customer surveys each week
• Identifying recurring complaints behind low scores
• Comparing NPS or CSAT with the previous period

Interpret Culture Index behavioral profiles to understand individual traits, team balance, and possible strain from workplace adaptation. Use PDF or JSON data to inform hiring discussions, management approaches, onboarding plans, and conflict mediation.

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• Understanding an individual’s behavioral profile
• Assessing team balance and missing roles
• Exploring strain from Survey and Job differences

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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Build market research evidence by comparing two market-size estimates, calculating survey sample sizes for each customer group, and scoring potential segments. Outputs show methods and assumptions so teams can assess the basis for business decisions.

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• Estimating market size for a business decision
• Planning survey responses for each customer segment
• Evaluating which market segments to pursue

Generate a complete academic literature survey from scratch using multi-agent orchestration. Searches academic databases (OpenAlex, CrossRef, Unpaywall), downloads PDFs, builds BibTeX, drafts a LaTeX review, compiles it, reviews for quality, and revises based on feedback — all automated. Use this skill whenever the user asks to "generate a literature review", "write a survey paper", "review recent papers on [topic]", "create a survey of [field]", "综述", "文献综述", "survey papers in top journals", or wants a multi-agent pipeline to produce a publishable-quality literature review. Also trigger when the user wants to search multiple journals for papers on a topic and synthesize findings into a structured document. Works for any academic field but optimized for economics journals.

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Tencent Survey (wj.qq.com) online survey platform skill. Supports survey creation, question management, answer collection, and data export. Use when user mentions surveys, questionnaires, forms, polls, exams, or assessments.

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End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.

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TRIGGER THIS when designing employee engagement surveys, analyzing employee feedback, creating action plans based on survey results, tracking engagement trends, planning pulse surveys, or improving employee experience. Designs comprehensive surveys that measure engagement, belonging, and satisfaction with actionable insights and accountability-driven improvement plans.

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Create and edit Google Forms, inspect existing forms, and read survey responses through an agent. The skill connects answers to their questions and retrieves every response when a complete set is requested.

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• Create a feedback survey
• Update an existing form's description
• Read survey responses alongside their questions

Turn interview notes, survey responses, and customer feedback into findings supported by evidence. Group recurring themes, assess frequency and impact, and produce prioritized recommendations to guide product improvements and future research.

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• Finding shared issues across user interviews
• Organizing themes in open-ended survey responses
• Prioritizing improvements from support tickets

Industrial AI literature research with mandatory intake questions, venue-aware source prioritization, structured report outputs, and survey draft generation. Use when the user needs up-to-date research on predictive maintenance, intelligent scheduling, industrial anomaly detection, smart manufacturing, cyber-physical systems, edge AI for automation, or crossover robotics-for-industry topics. Also trigger for adjacent terms: "digital twin", "industrial IoT", "Industry 4.0", "manufacturing AI", "factory automation", "process optimization", or "survey draft" in industrial contexts.

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Designs survey instruments, covering question wording, scale construction and index reliability, flow and treatment placement, pretesting and soft launches, respondent burden with attention and speeding rules, sensitive questions and social-desirability bias, and treatment delivery. Use when the user writes or revises survey questions, chooses a scale, orders blocks, plans a pilot, or worries respondents will not answer honestly. Indirect measurement goes to list-experiment, multi-country instruments to cross-national-design, and live-survey operations to qualtrics-ops.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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Analyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.

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Expert-level surveying covering leveling, traverses, coordinate geometry, GPS and GNSS, total station surveys, LiDAR, and construction layout.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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Survey related literature for a research topic or paper draft. Finds relevant papers, identifies citation gaps, and suggests key references. Use for "find related work", "survey papers on X", "what papers should I cite". 旧名 survey-literature。

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Design subscription retention workflows for customers who choose to cancel and those whose payments fail. The skill covers cancellation surveys, relevant offers, pauses, payment retries, reminder emails, usage-based warning signals, and metrics for evaluating retention efforts.

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• Designing a cancellation flow with a reason survey
• Matching pause or plan-change offers to cancellation reasons
• Planning payment retries and reminder emails

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.

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• Reviewing architecture after a batch of changes
• Investigating changes that touch too many files
• Prioritizing structural improvements

Turn interviews, surveys, usability notes, and customer feedback into shared themes, user groups, and prioritized recommendations. Findings include supporting quotes and participant counts, helping teams connect research evidence to concrete product improvements.

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• Find common issues across user interviews
• Combine survey results and support feedback
• Prioritize product improvements using research evidence