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Agent Skill · 研究检索

feature-domain-expert(特征领域专家)

feature-domain-expert 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

feature-domain-expert 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 58,252。

总安装 419周安装 18GitHub Stars 公开资料未说明下载 147核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:feature-domain-expert(特征领域专家)
来源仓库:https://github.com/fearovex/claude-config
仓库路径:skills/feature-domain-expert
安装命令:
npx skills add https://github.com/fearovex/claude-config --skill feature-domain-expert
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/fearovex/claude-config --skill feature-domain-expert

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

权威指导功能知识文件的编写与消费,聚焦领域模型一致性。区分永久性业务领域知识与临时性行为变更规范两种文件类型。防止 ai-context/features/ 与 SDD spec 内容重复的关键准则。适用于需要严格领域建模和知识沉淀的项目架构。通过 GitHub 安装,支持 Claude 等智能编程助手集成使用。

来源仓库内路径:skills/feature-domain-expert

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/fearovex/claude-config --skill feature-domain-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

feature-domain-expert 怎么安装?

在终端执行 npx skills add https://github.com/fearovex/claude-config --skill feature-domain-expert。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

feature-domain-expert 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

feature-domain-expert 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

feature-domain-expert 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

feature-domain-expert 用的人多吗?

公开数据是累计安装 419 次,近一周安装 18 次,下载 147 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

feature-domain-expert 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
feature-domain-expert

特征领域专家 · 本页

419—只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

feature-domain-expert

Authoritative guide for authoring and consuming feature knowledge files in ai-context/features/.

Triggers: feature file, domain knowledge, ai-context/features, bounded context, business rules, feature-domain-expert, domain invariants, domain context, feature doc


Patterns

Pattern 1: Feature file vs. SDD spec — the critical distinction

These two file types serve fundamentally different purposes and MUST NOT duplicate each other's content:

Aspectai-context/features/<domain>.mdSDD spec (engram artifact)
PurposePermanent business domain knowledgeBehavioral delta spec for a specific change
LifetimeLives forever — updated but never deletedCreated per change — eventually archived
ContentBusiness rules, invariants, data model, integration contracts, historyGIVEN/WHEN/THEN scenarios describing observable behavior for one change
When writtenOnce per bounded context; updated via /memory-updateOnce per SDD change; archived with the change
Who reads itsdd-propose Step 0, sdd-spec Step 0, human developerssdd-apply (acceptance criteria), sdd-verify
Cross-change valueHigh — encodes knowledge that predates and outlasts any single changeLow — describes behavior introduced or modified by one specific change

Rule: if you are writing GIVEN/WHEN/THEN scenarios, it belongs in a SDD spec artifact. If you are writing a business rule that will still be true five SDD cycles from now, it belongs in ai-context/features/.


Pattern 2: When to create a feature file

Create a new ai-context/features/<domain>.md when ALL of the following are true:

  1. A bounded context (a coherent area of business logic with its own vocabulary) has been identified — either by working in it during an SDD cycle or during memory-init.
  2. The domain has at least one business rule or invariant that is not captured in any spec file and that future SDD phases should know about.
  3. The domain is likely to be touched again in future SDD cycles (i.e., it is not a one-off technical implementation detail).

Do NOT create a feature file for:

  • Pure infrastructure concerns with no business rules (e.g., CI pipeline configuration).
  • A domain whose knowledge is already fully expressed in a small, stable spec file that will never be archived.
  • Domains that have not yet been explored — create a stub via memory-init and leave sections empty until knowledge is acquired.

Pattern 3: What belongs in each of the six sections

The canonical six sections in order — every feature file must contain all six:

1. Domain Overview Write 2–4 sentences describing what the bounded context does, who owns it, and what core responsibility it holds in the system. Focus on purpose and scope, not implementation details.

2. Business Rules and Invariants List the always-true constraints the domain enforces regardless of code path. Each item is a declarative statement that would still hold even if the implementation were rewritten from scratch. Examples: "A refund cannot exceed the original payment amount." "A user must have a verified email before making a purchase."

3. Data Model Summary Describe key entities, their relationships, and critical field constraints in plain prose or a small table. This is not a full schema — orient the reader to the most important entities and their constraints. Relationships between entities belong here.

4. Integration Points Document every external system, service, or domain this bounded context depends on or exposes an interface to. Use a table with columns: System/Service, Direction (inbound/outbound), Contract. Include async contracts (events, queues) and third-party dependencies.

5. Decision Log A chronological record of significant design or implementation decisions made for this domain. Each entry must state: what was decided, the rationale, and what it constrains going forward. Entries are NEVER deleted — they provide historical context for future developers.

6. Known Gotchas Unexpected behaviors, operational hazards, historical defects, or non-obvious constraints that a developer working in this domain must be aware of. Include things that caused bugs in the past, edge cases that are easy to miss, and anything that tripped up previous contributors.


Pattern 4: Domain slug matching heuristic (used by sdd-propose Step 0 and sdd-spec Step 0)

When an SDD phase runs for <change-name>, it determines which feature files to preload using this algorithm:

Input:  change-name (kebab-case string)
Output: list of matching ai-context/features/<domain>.md paths (may be empty)

Algorithm:
  1. If ai-context/features/ does not exist → return [] (skip silently)
  2. List all .md files in ai-context/features/
  3. Exclude any file whose name starts with underscore (e.g. _template.md)
  4. Extract stems: split change-name on "-", discard single-char stems
  5. For each remaining file f:
       domain = filename stem of f (without .md extension)
       if domain appears in change-name
         OR any stem from step 4 appears in domain:
         add f to matches
  6. Return all matches (may be multiple files)
  7. If matches is empty → skip preload silently (no error, no warning)

Examples:

Change nameStems (after filtering)Matches
add-payments-gateway[add, payments, gateway]features/payments.md — "payments" stem appears in domain
auth-token-refresh[auth, token, refresh]features/auth.md — "auth" stem appears in domain
feature-domain-knowledge-layer[feature, domain, knowledge, layer]No match against sdd-meta-system.md — none of the stems appear in "sdd-meta-system" and "sdd-meta-system" does not appear in the change name
improve-project-audit[improve, project, audit]No match — stems do not appear in any domain slug

Key behaviors:

  • _template.md is ALWAYS excluded — it is never a preload candidate.
  • The match is bidirectional: domain-in-change-name OR change-stem-in-domain.
  • Multiple files may match — all are loaded as enrichment context.
  • A non-match is NOT an error: the phase proceeds normally without domain context.

Pattern 5: Updating a feature file via /memory-update

Feature files follow an append-only update discipline. When /memory-update runs after a session that involved a domain with an existing feature file:

  1. Business Rules and Invariants: append newly discovered rules as new list items. Never remove or reword existing rules unless they are factually wrong (in which case add a correction note below the original rule instead of deleting it).
  2. Decision Log: append a new dated entry for any domain decision made during the session. Entries are never deleted or reordered.
  3. Known Gotchas: append new gotchas discovered during the session. Never remove a gotcha — even if the underlying bug was fixed, a note about the former behavior is useful history.
  4. Other sections: update Data Model Summary and Integration Points if new entities or integrations were introduced. Domain Overview may be updated if the scope of the domain changed significantly.

/memory-update MUST NOT create new feature files — it only updates existing ones. New feature files are created manually or scaffolded by memory-init.

Respect [auto-updated] section boundaries if present (same convention as in other ai-context files).


Pattern 6: Worked example — the sdd-meta-system domain

The canonical worked example for this skill is ai-context/features/sdd-meta-system.md in the agent-config repository. It demonstrates all six sections with realistic content for the SDD meta-system bounded context.

Below is an abbreviated illustration of the pattern each section should follow:

Domain Overview (2–4 sentences of purpose and scope):

"The SDD meta-system is the Claude Code configuration and skill orchestration framework... It provides two primary capabilities: a library of reusable skills and an SDD phase pipeline... The system is self-hosting: changes to its own skills must follow the same SDD cycle."

Business Rules and Invariants (declarative always-true statements):

- Every skill modification MUST go through the SDD planning cycle (at minimum /sdd-propose) before /sdd-apply.
- sync.sh MUST only move memory/ from ~/.claude/ to the repo.
- Developers MUST NOT edit files under ~/.claude/ directly.

Data Model Summary (table of key entities with constraints):

| Entity        | Key Fields                         | Constraints                               |
|---------------|------------------------------------|--------------------------------------------|
| Skill         | directory name, SKILL.md, format   | format must be procedural|reference|anti-pattern |
| SDD Change    | proposal, design, tasks             | stored in engram as sdd/<name>/* artifacts |

Integration Points (table of systems with direction and contract):

| System      | Direction | Contract                                              |
|-------------|-----------|-------------------------------------------------------|
| install.sh  | outbound  | Deploys repo to ~/.claude/ — run after any config change |
| sync.sh     | inbound   | Copies ~/.claude/memory/ to repo — memory only         |

Decision Log (chronological, with rationale and impact):

### 2026-03-03 — Add ai-context/features/ as Tier 1 domain knowledge layer
Decision: Introduce ai-context/features/<domain>.md as a permanent sub-layer...
Rationale: SDD phase skills lack access to stable business context between cycles...
Impact: sdd-propose and sdd-spec gain a non-blocking Step 0 that preloads matching feature files.

Known Gotchas (operational hazards and non-obvious behaviors):

- sync.sh does NOT deploy skills. Running it after a skill edit does nothing — run install.sh.
- Direct edits to ~/.claude/ are silently lost on the next install.sh run.

For the full worked example, read ai-context/features/sdd-meta-system.md.


Rules

  • A feature file MUST follow the canonical six-section structure defined in ai-context/features/_template.md. Sections must appear in this exact order: Domain Overview, Business Rules and Invariants, Data Model Summary, Integration Points, Decision Log, Known Gotchas.
  • Feature files MUST be named <domain-slug>.md where the slug is lowercase and hyphen-separated. No subdirectories are allowed inside ai-context/features/.
  • _template.md and any file with a leading underscore are excluded from the domain preload heuristic. They MUST NOT be loaded by SDD phases.
  • Feature files encode permanent domain knowledge — they are updated but NEVER deleted or archived. Do not confuse them with SDD spec artifacts (stored in engram as sdd/<change>/spec), which are created per change and eventually archived.
  • The domain preload step in sdd-propose and sdd-spec is non-blocking. A missing ai-context/features/ directory or a non-matching slug MUST NOT produce a warning or failure. The phase always proceeds normally.
  • /memory-update appends to feature files — it MUST NOT overwrite or delete existing content. /memory-update MUST NOT create new feature files; only scaffold them via memory-init or manual authoring.
  • project-analyze does NOT write to ai-context/features/. Feature files require domain expert judgment; they must not be auto-overwritten by a structural scan.
  • The feature_docs: block in project config is reserved for V2 audit integration. Do not activate it in V1.

平台分布

Codex

35.96%
按下载量换算53

Claude

28.86%
按下载量换算42

Cursor

18.19%
按下载量换算27

Gemini CLI

9.69%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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