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Agent Skill · 研究检索

spec-kitty-specify(规格小猫指定)

spec-kitty-specify 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。

spec-kitty-specify 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,权限为执行命令;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 54,843。

总安装 494周安装 21GitHub Stars 2下载 173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:spec-kitty-specify(规格小猫指定)
来源仓库:https://github.com/richfrem/agent-plugins-skills
仓库路径:skills/spec-kitty-specify
安装命令:
npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-specify
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/richfrem/agent-plugins-skills --skill spec-kitty-specify

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。

spec-kitty-specify 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于需要根据关键词或任务场景进行信息检索和筛选的工作流。通过关键词输入和来源线索匹配,输出结构化候选结果列表。

安装命令:npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-specify 建议确认权限范围和维护状态,注意可能触发联网或文件读写操作

来源仓库内路径:skills/spec-kitty-specify

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/richfrem/agent-plugins-skills --skill spec-kit…;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

spec-kitty-specify 怎么安装?

在终端执行 npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-specify。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

spec-kitty-specify 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。

spec-kitty-specify 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-specify 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

spec-kitty-specify 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。

spec-kitty-specify 用的人多吗?

公开数据是累计安装 494 次,近一周安装 21 次,下载 173 次,GitHub 2 星。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。

spec-kitty-specify 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。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
spec-kitty-specify

规格小猫指定 · 本页

4942执行命令未明确通过

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

🔗 Workflow Provenance

Source: This skill augments the baseline workflow located at ./workflows/spec-kitty.specify.md. It acts as an intelligent wrapper that is continuously improved with each execution.

/spec-kitty.specify - Create Feature Specification

Version: 0.11.0+

📍 WORKING DIRECTORY: Stay in the project root checkout

IMPORTANT: Specify works in the project root checkout. NO worktrees are created.

# Run from project root:
cd /path/to/project/root  # Your project root checkout

# All planning artifacts are created in the project root and committed:
# - kitty-specs/###-feature/spec.md → Created in project root
# - Committed to target branch (from create-feature JSON: target_branch/base_branch)
# - NO worktrees created

Worktrees are created later during /spec-kitty.implement, not during planning.

In repos with multiple features, always pass --feature <slug> to every spec-kitty command.

User Input

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Branch Strategy Confirmation (MANDATORY)

Before discovery, resolve branch intent through the Python helper, not by probing git directly:

spec-kitty agent feature branch-context --json

If the user already told you the intended landing branch, pass it explicitly:

spec-kitty agent feature branch-context --json --target-branch <intended-branch>

Parse the JSON and, in your next reply, explicitly tell the user:

  • Current branch at workflow start: current_branch
  • Default planning/base branch if you create the feature right now: planning_base_branch
  • Final merge target for completed changes: merge_target_branch
  • Whether branch_matches_target is true or false
  • If that is not the intended landing branch, stop and ask which branch should receive this feature before you run create-feature

Never talk generically about main or "the default branch". Name the actual branch values from the helper JSON. Do not shell out to git for this prompt.

DO NOT

  • Do not mix functional, non-functional, and constraint requirements in one list.
  • Do not emit requirements without stable IDs (FR-###, NFR-###, C-###).
  • Do not leave requirement status fields empty.
  • Do not write non-functional requirements without measurable thresholds.
  • Do not proceed to planning with unresolved requirement quality checklist failures.

Constitution Context Bootstrap (required)

Before discovery questions, load constitution context for this action:

spec-kitty constitution context --action specify --json
  • If JSON mode is bootstrap, treat JSON text as the initial governance context and consult referenced docs as needed.
  • If JSON mode is compact, proceed with concise governance context.

Discovery Gate (mandatory)

Before running any scripts or writing to disk you must conduct a structured discovery interview.

  • Scope proportionality (CRITICAL): FIRST, gauge the inherent complexity of the request:

- Trivial/Test Features (hello world, simple pages, proof-of-concept): Ask 1-2 questions maximum, then proceed. Examples: "a simple hello world page", "tic-tac-toe game", "basic contact form" - Simple Features (small UI additions, minor enhancements): Ask 2-3 questions covering purpose and basic constraints - Complex Features (new subsystems, integrations): Ask 3-5 questions covering goals, users, constraints, risks - Platform/Critical Features (authentication, payments, infrastructure): Full discovery with 5+ questions

  • User signals to reduce questioning: If the user says "just testing", "quick prototype", "skip to next phase", "stop asking questions" - recognize this as a signal to minimize discovery and proceed with reasonable defaults.
  • First response rule:

- For TRIVIAL features (hello world, simple test): Ask ONE clarifying question, then if the answer confirms it's simple, proceed directly to spec generation - For other features: Ask a single focused discovery question and end with WAITING_FOR_DISCOVERY_INPUT

  • If the user provides no initial description (empty command), stay in Interactive Interview Mode: keep probing with one question at a time.
  • Conversational cadence: After each user reply, decide if you have ENOUGH context for this feature's complexity level. For trivial features, 1-2 questions is sufficient. Only continue asking if truly necessary for the scope.

Discovery requirements (scale to feature complexity):

  1. Maintain a Discovery Questions table internally covering questions appropriate to the feature's complexity (1-2 for trivial, up to 5+ for complex). Track columns #, Question, Why it matters, and Current insight. Do not render this table to the user.
  2. For trivial features, reasonable defaults are acceptable. Only probe if truly ambiguous.
  3. When you have sufficient context for the feature's scope, paraphrase into an Intent Summary and confirm. For trivial features, this can be very brief.
  4. If user explicitly asks to skip questions or says "just testing", acknowledge and proceed with minimal discovery.

Mission Selection

After completing discovery and confirming the Intent Summary, determine the appropriate mission for this feature.

Available Missions

  • software-dev: For building software features, APIs, CLI tools, applications

- Phases: research → design → implement → test → review - Best for: code changes, new features, bug fixes, refactoring

  • research: For investigations, literature reviews, technical analysis

- Phases: question → methodology → gather → analyze → synthesize → publish - Best for: feasibility studies, market research, technology evaluation

Mission Inference

  1. Analyze the feature description to identify the primary goal:

- Building, coding, implementing, creating software → software-dev - Researching, investigating, analyzing, evaluating → research

  1. Check for explicit mission requests in the user's description:

- If user mentions "research project", "investigation", "analysis" → use research - If user mentions "build", "implement", "create feature" → use software-dev

  1. Confirm with user (unless explicit): "Based on your description, this sounds like a [software-dev/research] project. I'll use the [mission name] mission. Does that work for you?"
  2. Handle user response:

- If confirmed: proceed with selected mission - If user wants different mission: use their choice

  1. Handle --mission flag: If the user provides --mission <key> in their command, skip inference and use the specified mission directly.

Store the final mission selection in your notes and include it in the spec output. Do not pass a --mission flag to feature creation.

Workflow (0.11.0+)

Planning happens in the project root checkout - NO worktree created!

  1. Creates kitty-specs/###-feature/spec.md directly in project root
  2. Automatically commits to target branch
  3. No worktree created during specify

Worktrees created later: Use spec-kitty implement WP## to create a workspace for each work package. Worktrees are created later during implement (e.g., .worktrees/###-feature-WP##).

Location

  • Work in: Project root checkout (not a worktree)
  • Creates: kitty-specs/###-feature/spec.md
  • Commits to: target branch (from create-feature --json → target_branch)

Outline

0. Generate a Friendly Feature Title

  • Summarize the agreed intent into a short, descriptive title (aim for ≤7 words; avoid filler like "feature" or "thing").
  • Read that title back during the Intent Summary and revise it if the user requests changes.
  • Use the confirmed title to derive the kebab-case feature slug for the create-feature command.

The text the user typed after /spec-kitty.specify in the triggering message is the initial feature description. Capture it verbatim, but treat it only as a starting point for discovery—not the final truth. Your job is to interrogate the request, surface gaps, and co-create a complete specification with the user.

Given that feature description, do this:

  • Generation Mode (arguments provided): Use the provided text as a starting point, validate it through discovery, and fill gaps with explicit questions or clearly documented assumptions (limit [NEEDS CLARIFICATION: …] to at most three critical decisions the user has postponed).
  • Interactive Interview Mode (no arguments): Use the discovery interview to elicit all necessary context, synthesize the working feature description, and confirm it with the user before you generate any specification artifacts.
  1. Check discovery status:

- If this is your first message or discovery questions remain unanswered, stay in the one-question loop, capture the user's response, update your internal table, and end with WAITING_FOR_DISCOVERY_INPUT. Do not surface the table; keep it internal. Do not call the creation command yet. - Only proceed once every discovery question has an explicit answer and the user has acknowledged the Intent Summary. - Empty invocation rule: stay in interview mode until you can restate the agreed-upon feature description. Do not call the creation command while the description is missing or provisional.

  1. When discovery is complete and the intent summary, title, and mission are confirmed, run the feature creation command from repo root: spec-kitty agent feature create-feature "<slug>" --json Where <slug> is a kebab-case version of the friendly title (e.g., "Checkout Upsell Flow" → "checkout-upsell-flow"). The command returns JSON with: Parse these values for use in subsequent steps. All file paths are absolute. IMPORTANT: You must only ever run this command once. The JSON is provided in the terminal output - always refer to it to get the actual paths you're looking for. Immediately restate the branch contract to the user after parsing the JSON:

- result: "success" or error message - feature: Feature number and slug (e.g., "014-checkout-upsell-flow") - feature_dir: Absolute path to the feature directory inside the main repo - current_branch: the branch you started from - target_branch / base_branch: deterministic branch contract for downstream commands - planning_base_branch / merge_target_branch: explicit landing-branch aliases - branch_strategy_summary: human-readable summary of the branch contract - Current branch at start - Intended planning/base branch - Final merge target for later changes - Whether that matches the user's intended landing branch

  1. Stay in the main repository: No worktree is created during specify.
  2. Read the files created by create-feature: Do NOT try to read a template file. The spec structure is defined in this prompt (see sections below). The create-feature command scaffolds an initial spec.md — read it, then update it following the structure in this prompt.

- <feature_dir>/spec.md (already created, may be empty/template-filled) - <feature_dir>/meta.json (already created with feature identity metadata)

  1. Update <feature_dir>/meta.json only when needed: Example meta.json schema (identity fields that must be present explicitly): {"feature_number": "042", "slug": "my-feature", "feature_slug": "042-my-feature", "friendly_name": "My Feature", "mission": "software-dev", "target_branch": "main", "vcs": "git", "created_at": "2026-01-01T00:00:00+00:00"} Do not regenerate timestamps or directory paths via shell commands.

- Keep identity fields from create-feature unchanged (feature_number, slug, feature_slug, created_at, target_branch). - Keep target_branch aligned to the value from create-feature --json output. Never hardcode main. - Ensure friendly_name matches the confirmed title. - Ensure mission is correct. - Optionally add/update source_description. - Ensure vcs exists ("git" default).

  1. Generate the specification content by following this flow:

- Use the discovery answers as your authoritative source of truth (do not rely on raw $ARGUMENTS) - For empty invocations, treat the synthesized interview summary as the canonical feature description - Identify: actors, actions, data, constraints, motivations, success metrics - For any remaining ambiguity: - Ask the user a focused follow-up question immediately and halt work until they answer - Only use [NEEDS CLARIFICATION: …] when the user explicitly defers the decision - Record any interim assumption in the Assumptions section - Prioritize clarifications by impact: scope > outcomes > risks/security > user experience > technical details - Fill User Scenarios & Testing section (ERROR if no clear user flow can be determined) - Generate separated requirement tables: Functional (FR-###), Non-Functional (NFR-###), and Constraints (C-###) - Ensure each requirement entry has a status value and testable wording - Define Success Criteria (measurable, technology-agnostic outcomes) - Identify Key Entities (if data involved)

  1. Update the existing <feature_dir>/spec.md using the template structure, replacing placeholders with concrete details derived from the feature description while preserving section order and headings.
  2. Specification Quality Validation: After writing the initial spec, validate it against quality criteria: a. Create Spec Quality Checklist: Generate a checklist file at feature_dir/checklists/requirements.md using the checklist template structure with these validation items: ` # Specification Quality Checklist: [FEATURE NAME] **Purpose**: Validate specification completeness and quality before proceeding to planning **Created**: [DATE] **Feature**: [Link to spec.md] ## Content Quality - [] No implementation details (languages, frameworks, APIs) - [] Focused on user value and business needs - [] Written for non-technical stakeholders - [] All mandatory sections completed ## Requirement Completeness - [] No [NEEDS CLARIFICATION] markers remain - [] Requirements are testable and unambiguous - [] Requirement types are separated (Functional / Non-Functional / Constraints) - [] IDs are unique across FR-###, NFR-###, and C-### entries - [] All requirement rows include a non-empty Status value - [] Non-functional requirements include measurable thresholds - [] Success criteria are measurable - [] Success criteria are technology-agnostic (no implementation details) - [] All acceptance scenarios are defined - [] Edge cases are identified - [] Scope is clearly bounded - [] Dependencies and assumptions identified ## Feature Readiness - [] All functional requirements have clear acceptance criteria - [] User scenarios cover primary flows - [] Feature meets measurable outcomes defined in Success Criteria - [] No implementation details leak into specification ## Notes - Items marked incomplete require spec updates before /spec-kitty.plan ` b. Run Validation Check: Review the spec against each checklist item: c. Handle Validation Results: d. Update Checklist: After each validation iteration, update the checklist file with current pass/fail status

- For each item, determine if it passes or fails - Document specific issues found (quote relevant spec sections) - If all items pass: Mark checklist complete and proceed to step 6 - If items fail (excluding [NEEDS CLARIFICATION]): 1. List the failing items and specific issues 2. Update the spec to address each issue 3. Re-run validation until all items pass (max 3 iterations) 4. If still failing after 3 iterations, document remaining issues in checklist notes and warn user - If [NEEDS CLARIFICATION] markers remain: 1. Extract all [NEEDS CLARIFICATION:...] markers from the spec 2. Re-confirm with the user whether each outstanding decision truly needs to stay unresolved. Do not assume away critical gaps. 3. For each clarification the user has explicitly deferred, present options using plain text—no tables: Question [N]: [Topic] Context: [Quote relevant spec section] Need: [Specific question from NEEDS CLARIFICATION marker] Options: (A) [First answer — implications] · (B) [Second answer — implications] · (C) [Third answer — implications] · (D) Custom (describe your own answer) Reply with a letter or a custom answer. 4. Number questions sequentially (Q1, Q2, Q3 - max 3 total) 5. Present all questions together before waiting for responses 6. Wait for user to respond with their choices for all questions (e.g., "Q1: A, Q2: Custom - [details], Q3: B") 7. Update the spec by replacing each [NEEDS CLARIFICATION] marker with the user's selected or provided answer 8. Re-run validation after all clarifications are resolved

  1. Report completion with feature directory, spec file path, checklist results, and readiness for the next phase (/spec-kitty.plan).

NOTE: The script creates and checks out the new branch and initializes the spec file before writing.

General Guidelines

Quick Guidelines

  • Focus on WHAT users need and WHY.
  • Avoid HOW to implement (no tech stack, APIs, code structure).
  • Written for business stakeholders, not developers.
  • DO NOT create any checklists that are embedded in the spec. That will be a separate command.

Section Requirements

  • Mandatory sections: Must be completed for every feature
  • Optional sections: Include only when relevant to the feature
  • When a section doesn't apply, remove it entirely (don't leave as "N/A")

For AI Generation

When creating this spec from a user prompt:

  1. Make informed guesses: Use context, industry standards, and common patterns to fill gaps
  2. Document assumptions: Record reasonable defaults in the Assumptions section
  3. Limit clarifications: Maximum 3 [NEEDS CLARIFICATION] markers - use only for critical decisions that:

- Significantly impact feature scope or user experience - Have multiple reasonable interpretations with different implications - Lack any reasonable default

  1. Prioritize clarifications: scope > security/privacy > user experience > technical details
  2. Think like a tester: Every vague requirement should fail the "testable and unambiguous" checklist item
  3. Common areas needing clarification (only if no reasonable default exists):

- Feature scope and boundaries (include/exclude specific use cases) - User types and permissions (if multiple conflicting interpretations possible) - Security/compliance requirements (when legally/financially significant)

Examples of reasonable defaults (don't ask about these):

  • Data retention: Industry-standard practices for the domain
  • Performance targets: Standard web/mobile app expectations unless specified
  • Error handling: User-friendly messages with appropriate fallbacks
  • Authentication method: Standard session-based or OAuth2 for web apps
  • Integration patterns: RESTful APIs unless specified otherwise

Success Criteria Guidelines

Success criteria must be:

  1. Measurable: Include specific metrics (time, percentage, count, rate)
  2. Technology-agnostic: No mention of frameworks, languages, databases, or tools
  3. User-focused: Describe outcomes from user/business perspective, not system internals
  4. Verifiable: Can be tested/validated without knowing implementation details

Good examples:

  • "Users can complete checkout in under 3 minutes"
  • "System supports 10,000 concurrent users"
  • "95% of searches return results in under 1 second"
  • "Task completion rate improves by 40%"

Bad examples (implementation-focused):

  • "API response time is under 200ms" (too technical, use "Users see results instantly")
  • "Database can handle 1000 TPS" (implementation detail, use user-facing metric)
  • "React components render efficiently" (framework-specific)
  • "Redis cache hit rate above 80%" (technology-specific)

平台分布

Codex

31.72%
按下载量换算55

Claude

29.44%
按下载量换算51

Cursor

20.06%
按下载量换算35

Gemini CLI

9.94%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-specify 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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