「goals」の検索結果

Design and verify a deterministic operational-value grader for any GitHub Agentic Workflow. Use when reasoning from workflow goals to measurable downstream outcomes, defining repository evidence, choosing outcome metrics, or creating an operational-value evaluator. Usage: /operational-value-designer OWNER/REPO WORKFLOW-NAME.

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A structured workflow for co-authoring high-signal docs (PRD, RFC, design docs, proposals, decision records). Use when the user needs to turn messy context into a readable artifact with clear goals, tradeoffs, and next steps. Emphasizes context capture, outline-first drafting, and context-free reader testing to catch blind spots.

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Reviews whether a design is SOUND — solving the right problem, derived from its stated goals and constraints — and emits ranked, evidence-backed findings, not edits. Read when asked to 'review this design', 'is this design sound', 'pressure-test this proposal', 'do a design review', 'does this solve the right problem', 'poke holes in this spec', 'should we build this', or to critique a proposal / spec / ADR / architecture or product decision. Do NOT read when the user wants to AUTHOR one of these — routing a new proposal is frame-a-proposal, a spec is write-a-spec, a decision record is record-a-decision, a postmortem is write-a-postmortem. Do NOT read for code review of a diff, or to fact-check individual claims (that is a correctness pass, a different job).

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Evaluates your campaign and ad set structure against your actual goals and budget. Flags over-segmentation that fragments your data, under-segmentation that hides performance differences, budget allocation issues, and consolidation opportunities that would improve algorithmic delivery and your ability to optimize. Platform: Google and Meta.

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Diagnose BB-specific Codex session controls, model acceptance, and durable goals.

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Run tasks in a loop until goals are met — use for iterative refinement, polling, or convergence

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Goal-based workflow orchestration - routes tasks to specialist agents based on user goals

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星火燎原：从零起步、资源极少时，先评估客观条件，选定一个能站稳、能复用、能扩展的"根据地"（最小切入点），扎根后再逐步扩张，拒绝四处出击的流寇主义。当启动新项目、做 MVP / pilot、面对宏大目标不知从何下手、或做了很多零散事却没有积累时触发；项目已有稳固基础、只是修改现有系统时不触发。 English: Spark a prairie fire. When starting from almost nothing, assess the conditions, choose one base area (a minimal foothold that can hold, be reused, and expand), dig in, then grow step by step instead of scattering effort. Trigger for new projects, MVPs, pilots, daunting goals with no obvious first step, or busy work that accumulates nothing; skip when a solid base already exists or the task modifies an existing system.

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统筹兼顾：当多个目标、指标或利益方相互制约、优化一项会伤害另一项时，识别全部辩证关系对，检查片面性，为每对关系定出当前阶段的侧重与底线，评估系统性影响，并设失衡预警。触发信号包括 trade-off、目标冲突、速度 vs 质量、短期 vs 长期、"顾此失彼"；只有单一目标、用户已定取舍、或属硬性二选一时不触发。 English: Overall planning. When several goals, metrics, or stakeholders constrain each other and optimizing one harms another, map every dialectical pair, check for one-sidedness, set the current emphasis and floor for each pair, assess system-wide effects, and define imbalance alarms. Triggers include trade-offs, goal conflicts, speed vs quality, short vs long term; skip for single-goal work, decisions the user already made, or hard either/or constraints.

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This skill should be used when the user asks to "write a post", "check my voice", "look up contact", "prepare for meeting", "weekly review", "track goals", or mentions personal brand, content creation, network management, or voice consistency.

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Surface the most valuable next action by combining task stack, queue state, inbox pressure, health, and goals. Recommends one specific action with rationale. Triggers on "/next", "what should I do", "what's next".

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Get research-backed architecture advice for your knowledge system. Describe your use case, constraints, and goals — get specific recommendations grounded in TFT research with rationale for each decision. Triggers on "/recommend", "what would you recommend", "architecture advice", "knowledge system for".

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Explaining how the business is doing, meaning why a metric moved, which segment drove it, pace against a comparable, progress against goals the operator has stated, and questions over sales, traffic, conversion, and customers or this operation's equivalents that the snapshot alone does not answer. Not needed when the snapshot's headline figures and their change on the prior period answer the question, how a period went included.

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Teaching a customer how to choose before any candidates appear, when they ask what matters in a category, how two approaches to it differ, or where to begin. Not needed once the customer has named the thing they want, however considered the purchase (that is search work), nor for outfitting a space or an event (planning-goals).

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Planning a coordinated set of items toward a goal, an event, a trip, or a project, where the request only comes together as several purchases with a structure between them, and later changes to a plan you have presented. Not needed when the customer wants a single item, however carefully chosen.

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Build research-grounded personas with goals, frustrations, and behavioural patterns. Use when decisions need a consistent user reference. For one session's emotional snapshot use `empathy-map`; for motivation framing use `jobs-to-be-done`.

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Write rationale connecting decisions to user needs, business goals, and principles. Use when a decision needs defending in writing. For a live conversation, use `design-negotiation`.

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Advocate for design quality, scope, and timeline with partners and leadership using evidence and shared goals. Use in the conversation itself. For the commercial vocabulary behind it, use `business-design` (ux-strategy).

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Build a Says, Thinks, Does, Feels map for one user or segment. Use when sharing user understanding quickly. For a composite archetype with goals and behaviours use `user-persona`; for cross-session themes use `affinity-diagram`.

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Analyze and tune AssemblyAI model choice, audio delivery, polling, Streaming v3 turns, concurrency, and downstream work. Use when pursuing measured latency or throughput goals. Trigger with "tune AssemblyAI" or "AssemblyAI latency".

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Format and structurally validate local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

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Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

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Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.

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Rewrites technical research content into a structured lay summary that cross-disciplinary teams can quickly understand and act on. Use when the user wants to explain research to colleagues outside their specialty — clinicians, wet-lab scientists, bioinformaticians, product managers, or leadership. Trigger on: "lay summary", "explain my research to the team", "non-technical summary", "cross-disciplinary summary", "translate my findings", "align our team on the study", or any request to communicate research goals, findings, or next steps to a mixed or non-specialist audience. Part of the AIPOCH Academic Writing skill hub. Sits midstream: after research content is clarified, before downstream deliverables like slide decks or graphical abstracts.

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。