「elicit」の検索結果

ALWAYS use this skill whenever you would otherwise ask the user a question in free-form chat -- for clarifications, confirmations (especially destructive actions), missing parameters, multiple-choice decisions, or structured form input. Elicitations are routed through the Unique AI Platform UI via `unique-cli elicit create` + `elicit wait` (the default polling pattern) or a single supervised `unique-cli elicit ask` call (only when your agent instructions say your environment supervises the wait) so the user gets a proper structured prompt and you get a structured answer back. Do NOT ask the user in plain chat when you can use this skill instead.

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Manual intent elicitation skill. Use when the user asks to clarify a fuzzy idea, run an elicitation session, design an agent/skill/workflow, identify strategic tension, do a postmortem, align taste/brand direction, or turn vague judgment into an actionable Intent Brief. The skill asks 3–5 sharp questions first, then compiles answers into an Intent Brief and routes the result into system/workflow change. Never run automatically or on a schedule.

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Facilitate structured requirements gathering. Use when the user says "gather requirements", "elicitation session", "stakeholder interview", "requirements workshop", "what do the users need", "discover requirements", "trawl for requirements", "understand the business need", "conduct interviews", or needs to systematically extract requirements from stakeholders - even if they don't explicitly say "elicitation".

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Build Model Context Protocol (MCP) servers in C#/.NET against the current ModelContextProtocol 2.x NuGet packages. Helps with cases the model gets wrong without guidance — stale versions (0.x preview or 1.x-era defaults), the v2 stateless-by-default HTTP flip, the 2026-07-28 spec deprecations (roots/sampling/logging), MCP Apps and Tasks extension packages, elicitation URL mode, per-session HTTP wiring, OAuth and reverse-proxy deploy specifics, and debugging MapMcp / STDIO / Streamable-HTTP errors. Also covers STDIO and Streamable HTTP transports (SSE is deprecated), tools, prompts, resources, completions, and a basic .NET MCP client. Trigger when the user says or implies any .NET MCP server work: ModelContextProtocol, McpServerTool, MapMcp, WithStdioServerTransport, "MCP server in C#", "MCP tool in dotnet", "expose this as MCP", or names a primitive (prompt/resource/elicitation/MCP App) in a .NET context. Skip for MCP work in other languages.

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Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

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Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.

日本語の概要は準備中です。原文の説明を表示しています。

Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.

日本語の概要は準備中です。原文の説明を表示しています。

Run a structured discovery session to build an Allium specification through conversation. Use when the user wants to create a new spec from scratch, elicit or gather requirements, capture domain behaviour, specify a feature or system, define what a system should do, or is describing functionality and needs help shaping it into a specification.

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Eliciting, documenting and structuring requirements for a Salesforce implementation: stakeholder discovery interviews, As-Is and To-Be process mapping, and gap analysis against standard Salesforce capability. Trigger keywords: requirements gathering, user story, As-Is To-Be, gap analysis, stakeholder interview, process mapping, business requirements, fit gap. NOT for authoring the user stories themselves - use admin/user-story-writing-for-salesforce. NOT for scoring requirements against a specific org - use admin/fit-gap-analysis-against-org. NOT for technical design decisions - use architect/solution-design-patterns. Also covers the requirements catalogue itself (one row per requirement, MoSCoW priority, source stakeholder, downstream target) and Salesforce non-functional requirements: record volume, sharing layer, integration pattern, reporting joins, mobile/offline, and licence implication.

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Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).

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Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.

日本語の概要は準備中です。原文の説明を表示しています。

Structured interview and observation techniques for extracting tacit knowledge from domain experts

日本語の概要は準備中です。原文の説明を表示しています。

Methodology for eliciting expert knowledge about cognitive processes, decisions, and mental models in complex tasks

日本語の概要は準備中です。原文の説明を表示しています。

Use FIRST when user requests any disease market sizing report (e.g. "做 X 市场调研" / "Run market sizing for X"). Elicits 4-6 startup questions (evidence window, geography, international comparison, report depth, user role) and writes .cache/<slug>/contract.json. ALWAYS invoke before disease-market-sizing-orchestration. Validates against schemas/contract.schema.json.

日本語の概要は準備中です。原文の説明を表示しています。

Plan, run, and measure an adversary engagement operation using the MITRE Engage framework so that deployed deception is driven by strategy instead of deployed ad hoc. Covers the Engage Matrix (Prepare, Expose, Affect, Elicit, Understand), the 10-Step Operational Process, mapping engagement Activities to the ATT&CK techniques they expose, and defining measurable Goals and Operational Objectives. Use when a team has honeypots, honeytokens, or canary tokens but no coordinating strategy, when leadership asks "should we engage attackers and how", when building a deception/denial program, when writing an adversary engagement operation plan, or when deciding which deception Activities to deploy against a specific threat actor. Keywords: MITRE Engage, adversary engagement, cyber deception strategy, denial and deception, Engage Matrix, EAC, EGO, Expose Affect Elicit, deception program, honeypot strategy, engagement operation.

日本語の概要は準備中です。原文の説明を表示しています。

Guide the user through starting a new web application project — elicit requirements (MoSCoW scoping), create architecture and requirements documents, break work into tasks, and register them as a GitHub Project kanban board. Use when the user says "新しいWebアプリを作りたい", "Webアプリの立ち上げを手伝って", "start a new web app project", "プロジェクトの要件を整理したい", or "GitHub Projectにタスクを登録したい".

Structured interview and observation techniques for extracting tacit knowledge from domain experts

日本語の概要は準備中です。原文の説明を表示しています。

Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.

日本語の概要は準備中です。原文の説明を表示しています。

Apply rapport-based elicitation to resistant or semi-cooperative humans and agents. Use when coercive prompting or brittle handoffs damage cooperation. NOT for routine cooperative tasks, pure technical debugging, or already reliable exchanges.

日本語の概要は準備中です。原文の説明を表示しています。

Methodology for eliciting expert knowledge about cognitive processes, decisions, and mental models in complex tasks

日本語の概要は準備中です。原文の説明を表示しています。

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team

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This skill covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".

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Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV, ELPD, stacking weights), hierarchical/multilevel models, count regressions, logistic regression with uncertainty, prior sensitivity analysis, reporting Bayesian results, or mentions of PyMC, ArviZ, InferenceData, credible intervals, posterior distributions, shrinkage, uncertainty quantification. Also trigger for model comparison, diagnosing sampling problems, choosing priors, or presenting stats to non-technical audiences.

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Identify tumor neoantigens from somatic mutations using pVACtools for personalized cancer immunotherapy. Predict mutant peptides that bind patient HLA and may elicit T-cell responses. Use when identifying vaccine targets or checkpoint inhibitor response biomarkers from tumor sequencing data.

日本語の概要は準備中です。原文の説明を表示しています。