职场效率
#design
product-design
Use for new surfaces, redesigns and vague product asks: establish user tasks and business meaning. Built-screen audits and visual polish have separate skills.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=lobehub-lobehub-agents-skills-product-design-skill-md&format=skill
下载 .skill
标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name product-design description Use for new surfaces, redesigns and vague product asks: establish user tasks and business meaning. Built-screen audits and visual polish have separate skills. Product Design Decide what to build and why before arguing about spacing. Two rules are non-negotiable: Never design from the surface. Establish what the business actually models first. Never turn the business model into the information architecture. The domain constrains what the surface may claim; the user's retrieval and decision task determines how the surface is organized. ( P-14 ) This skill handles product meaning first. It may hand a grounded frame to ux or design-prototype , but discovery does not require implementation or a prototype. Strip out every framework, table and component name: if no product insight remains, the finding belongs in an engineering skill. Relationship to other skills Skill Question When product-design What should this surface be, and why? Before or during framing ux How should the interaction behave and feel? While designing or building ux-audit Does the built surface honor those rules? After it exists design-prototype How can a materialized interaction be tested? When a prototype is useful Select the smallest run mode Mode Finish line Discover Evidence map, business model, user-model hypotheses and structural diagnosis Frame Discover + view model, information architecture, scope and open decisions Materialize Frame + a requested spec and/or prototype Do not create or modify artifacts unless the user asked for a design artifact, prototype or repository change. For a substantial run, read references/pattern-base.md and references/layer-model.md in full. For a narrow question, inspect only the relevant patterns and state what scope was checked. Step 1 — Ground the business model Establish what is true before diagnosing the surface. For broad repositories, parallelize independent evidence areas when delegation is available and useful; the primary agent remains responsible for reconciling contradictions. Produce: Concepts, roles and states, including variants the current surface hides ( P-04 ). Who produces the terminal business fact and what closes the lifecycle ( P-12 ). What each state obliges someone to do ( P-01 ). What business event each action produces ( P-02 ). Concepts the business does not have ( P-06 ). Evidence sources, contradictions and confidence. Treat domain types and state machines as strong evidence, not the sole source of truth. Check relevant service behavior, permissions, policy, production facts and user evidence when available. Code can be stale, accidental or incomplete. Never proceed from the team's assumed model of the product. Treat it as a hypothesis until checked against domain and user evidence. Step 2 — Frame the user's view model Grounding constrains what the product may claim; it does not determine how a person should see it. Separate observed user evidence from design hypotheses: Circumstance and job — “When I open this, I need to …”. Lookup objects — stable things the user scans, compares or inspects. Attached proof — what verifies each object without reconstructing joins. First scan — the 2–4 questions answered without a click. Secondary dimensions — chronology, provenance, runs and internal states; keep them as filters or drill-down unless the job is execution audit. Evidence and confidence — mark claims as observed, reported or inferred; state how consequential assumptions can be validated. Model Role in design Business/domain model Defines truth, validity, completeness and red lines User view model Defines grouping, ordering, labels and default expansion Execution model Supplies provenance and audit detail; never gets the page for free Do not proceed while proposed top-level sections are merely domain or execution nouns such as runs , reports , evidence versions or table names. Rewrite them in the objects the user is trying to find ( P-14 ). Step 3 — Diagnose the structural error Name something wrong regardless of taste, for example: A decision inbox exposes only errors, so it reads as an error log ( P-04 ). A button promises a business event that does not exist ( P-05 ). One page is both triage desk and reading room, and fails at both ( P-07 ). If no structural error can be supported by evidence, report that instead of manufacturing a diagnosis. Step 4 — Align decisions by dependency Surface only decisions that change the shape of the answer. For each, give a recommendation, evidence and consequence. Resolve upstream decisions before the downstream ones they constrain. Ask a blocking question separately when its answer would materially change the work; group independent decisions in one memo. When the user authorizes autonomous progress, proceed with the recommended default and label the assumption. Never ask the user for something available through grounding. For review, approval or acceptance surfaces, establish first whether the system or a human decision closes the lifecycle ( P-12 ). Step 5 — Prototype when it resolves uncertainty Use design-prototype only when the user requests a prototype or interaction, density or hierarchy cannot be resolved honestly in a written frame. This belongs to Materialize mode. Use plausible data and include empty, blocked and at-scale states. Tag anything unsupported by the business with NEW on the mock. Treat corrections as evidence. They become Pattern Base candidates only when they generalize beyond the current surface. Step 6 — Scope honestly Bucket Meaning ✅ Already modeled and exposed Rearranging an existing capability ⚠️ Modeled, not exposed Lower domain risk; engineering cost still needs validation ❌ Not modeled Requires domain expansion and explicit cost/risk assessment Prefer a coherent ✅ + ⚠️ slice when it solves a complete user job. Do not ship an incomplete or misleading slice merely because its concepts already exist. A ❌ capability may be the right first investment when required for value, safety or semantic completeness ( P-10 ). Name every ❌ item and its likely cost drivers in the spec ( P-11 ). Do not silently drop it, but do not pretend its cost can be inferred from the domain model alone. Close — record coverage and propose learning Close at the level authorized by the user: If producing a spec, include the reality-check log from references/trace-schema.md . For read-only work, report important assumptions and evidence in the response instead of writing. Compare unexpected findings with references/canon.md and propose a Pattern Base candidate only when the lesson is general. Append to references/pattern-base.md only when repository edits are authorized and the candidate is deduplicated and reviewable. Record the coverage signal: sources and lifecycle areas checked, unavailable evidence, remaining low-confidence claims, and whether a new gap was found. Zero new gaps means only that none were found within the inspected scope. It does not prove that the implementation or mental model is exhausted. Deliverables Artifact When Template/location Design spec Requested in Materialize mode templates/design-spec.md ; use a user-specified path or ask Interactive prototype Requested or needed to resolve uncertainty Beside the spec, via design-prototype Reality-check log Inside a produced spec references/trace-schema.md Pattern candidate A general, unexpected lesson emerges In the spec/response; append only after review and authorization Anti-patterns Designing from a screenshot or treating the domain model as the sitemap. Reading a state or action by its label rather than its obligation or event. Inferring the user model from code and presenting it as observed truth. Treating a missing concept as a layout problem. Asking questions the available evidence already answers. Treating concept existence as an engineering cost estimate. Automatically prototyping, writing a spec or mutating the Pattern Base. Adding local corrections or engineering findings to shared product guidance. For a complete example, read references/worked-example.md .
Agent 识别该技能的关键词,点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |