Token导航 LogoToken导航

Agent Skill · 研究检索

spec-kitty-plan(规格小猫计划)

spec-kitty-plan 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-plan 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,权限为需要联网;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 55,255。

总安装 489周安装 21GitHub Stars 2下载 171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-plan(规格小猫计划)
来源仓库:https://github.com/richfrem/agent-plugins-skills
仓库路径:skills/spec-kitty-plan
安装命令:
npx skills add https://github.com/richfrem/agent-plugins-skills --skill spec-kitty-plan
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-plan

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-plan 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-plan 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-plan

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

    它需要需要联网,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-plan 怎么安装?

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

spec-kitty-plan 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-plan 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

spec-kitty-plan 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-plan 用的人多吗?

公开数据是累计安装 489 次,近一周安装 21 次,下载 171 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-plan 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

  • 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

  • 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-plan

规格小猫计划 · 本页

4892需要联网未明确通过

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.plan.md. It acts as an intelligent wrapper that is continuously improved with each execution.

/spec-kitty.plan - Create Implementation Plan

Version: 0.11.0+

📍 WORKING DIRECTORY: Stay in the project root checkout

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

# Run from project root (same directory as /spec-kitty.specify):
# You should already be here if you just ran /spec-kitty.specify

# Creates:
# - kitty-specs/###-feature/plan.md → In project root checkout
# - Commits to target branch
# - NO worktrees created

Do NOT cd anywhere. Stay in the project root checkout root.

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 asking planning questions or generating artifacts, you must make the branch contract explicit.

  • Never describe the landing branch vaguely. Always name the actual branch value.
  • If the user says the feature should land somewhere else, stop and resolve that before writing plan.md.
  • You must repeat the branch contract twice during this command:

1. immediately after parsing setup-plan --json 2. again in the final report before suggesting /spec-kitty.tasks

Constitution Context Bootstrap (required)

Before planning interrogation, load constitution context for this action:

spec-kitty constitution context --action plan --json
  • If JSON mode is bootstrap, apply JSON text as first-run governance context and follow referenced docs as needed.
  • If JSON mode is compact, continue with condensed governance context.

Location Check (0.11.0+)

This command runs in the project root checkout, not in a worktree.

  • Resolve branch context from deterministic JSON output, not from meta.json inspection:

- Run spec-kitty agent feature setup-plan --feature <feature-slug> --json - Use current_branch, target_branch / base_branch, and planning_base_branch / merge_target_branch (plus uppercase aliases) from that payload - Use branch_matches_target from that payload to detect branch mismatch; do not probe branch state manually inside the prompt

  • Planning artifacts live in kitty-specs/###-feature/
  • The plan template is committed to the target branch after generation

Path reference rule: When you mention directories or files, provide either the absolute path or a path relative to the project root (for example, kitty-specs/<feature>/tasks/). Never refer to a folder by name alone.

Planning Interrogation (mandatory)

Before executing any scripts or generating artifacts you must interrogate the specification and stakeholders.

  • Scope proportionality (CRITICAL): FIRST, assess the feature's complexity from the spec:

- Trivial/Test Features (hello world, simple static pages, basic demos): Ask 1-2 questions maximum about tech stack preference, then proceed with sensible defaults - Simple Features (small components, minor API additions): Ask 2-3 questions about tech choices and constraints - Complex Features (new subsystems, multi-component features): Ask 3-5 questions covering architecture, NFRs, integrations - Platform/Critical Features (core infrastructure, security, payments): Full interrogation with 5+ questions

  • User signals to reduce questioning: If the user says "use defaults", "just make it simple", "skip to implementation", "vanilla HTML/CSS/JS" - recognize these as signals to minimize planning questions and use standard approaches.
  • First response rule:

- For TRIVIAL features: Ask ONE tech stack question, then if answer is simple (e.g., "vanilla HTML"), proceed directly to plan generation - For other features: Ask a single architecture question and end with WAITING_FOR_PLANNING_INPUT

  • If the user has not provided plan context, keep interrogating with one question at a time.
  • Conversational cadence: After each reply, assess if you have SUFFICIENT context for this feature's scope. For trivial features, knowing the basic stack is enough. Only continue if critical unknowns remain.

Planning requirements (scale to complexity):

  1. Maintain a Planning Questions table internally covering questions appropriate to the feature's complexity (1-2 for trivial, up to 5+ for platform-level). Track columns #, Question, Why it matters, and Current insight. Do not render this table to the user.
  2. For trivial features, standard practices are acceptable (vanilla HTML, simple file structure, no build tools). Only probe if the user's request suggests otherwise.
  3. When you have sufficient context for the scope, summarize into an Engineering Alignment note and confirm.
  4. If user explicitly asks to skip questions or use defaults, acknowledge and proceed with best practices for that feature type.

Outline

  1. Check planning discovery status:

- If any planning questions remain unanswered or the user has not confirmed the Engineering Alignment summary, stay in the one-question cadence, capture the user's response, update your internal table, and end with WAITING_FOR_PLANNING_INPUT. Do not surface the table. Do not run the setup command yet. - Once every planning question has a concrete answer and the alignment summary is confirmed by the user, continue.

  1. Resolve feature context deterministically (CRITICAL - prevents wrong feature selection):

- Prefer an explicit feature slug from user direction or from the current directory path (kitty-specs/<feature-slug>/...) - If you do not yet have an explicit feature slug, run spec-kitty agent feature setup-plan --json once without --feature - If that call succeeds, treat its JSON as the canonical setup payload and skip step 3 - If that call returns an ambiguity error with available_features, stop and resolve one explicit feature slug before continuing

  1. Setup: If step 2 did not already return a successful setup payload, run spec-kitty agent feature setup-plan --feature <feature-slug> --json from the repository root and parse JSON for: Before proceeding, explicitly state to the user: Example: # If detected feature is 020-my-feature: spec-kitty agent feature setup-plan --feature 020-my-feature --json Error handling: If the command fails with "Cannot detect feature" or "Multiple features found", verify your feature detection logic in step 2 and ensure you're passing the correct feature slug.

- result: "success" or error message - feature_slug: Resolved feature slug - spec_file: Absolute path to resolved spec.md - plan_file: Absolute path to the created plan.md - feature_dir: Absolute path to the feature directory - current_branch: branch checked out when planning started - target_branch / base_branch (deterministic branch contract for downstream commands) - planning_base_branch / merge_target_branch: explicit aliases for planning and merge intent - branch_strategy_summary: canonical sentence describing the branch strategy - Current branch at plan start - Intended planning/base branch - Final merge target for completed changes - Whether branch_matches_target says the current branch matches that intended target

  1. Load context: Read spec_file from setup-plan JSON output and .kittify/constitution/constitution.md if it exists. If the constitution file is missing, skip Constitution Check and note that it is absent. Load IMPL_PLAN template (already copied).
  2. Execute plan workflow: Follow the structure in IMPL_PLAN template, using the validated planning answers as ground truth:

- Update Technical Context with explicit statements from the user or discovery research; mark [NEEDS CLARIFICATION: …] only when the user deliberately postpones a decision - If a constitution exists, fill Constitution Check section from it and challenge any conflicts directly with the user. If no constitution exists, mark the section as skipped. - Evaluate gates (ERROR if violations unjustified or questions remain unanswered) - Phase 0: Generate research.md (commission research to resolve every outstanding clarification) - Phase 1: Generate data-model.md, contracts/, quickstart.md based on confirmed intent - Phase 1: Update agent context by running the agent script - Re-evaluate Constitution Check post-design, asking the user to resolve new gaps before proceeding

  1. STOP and report: This command ends after Phase 1 planning. Report branch, IMPL_PLAN path, and generated artifacts. ⚠️ CRITICAL: DO NOT proceed to task generation! The user must explicitly run /spec-kitty.tasks to generate work packages. Your job is COMPLETE after reporting the planning artifacts.

Phases

Phase 0: Outline & Research

  1. Extract unknowns from Technical Context above:

- For each NEEDS CLARIFICATION → research task - For each dependency → best practices task - For each integration → patterns task

  1. Generate and dispatch research agents: For each unknown in Technical Context: Task: "Research {unknown} for {feature context}" For each technology choice: Task: "Find best practices for {tech} in {domain}"
  2. Consolidate findings in research.md using format:

- Decision: [what was chosen] - Rationale: [why chosen] - Alternatives considered: [what else evaluated]

Output: research.md with all NEEDS CLARIFICATION resolved

Phase 1: Design & Contracts

Prerequisites: research.md complete

  1. Extract entities from feature spec → data-model.md:

- Entity name, fields, relationships - Validation rules from requirements - State transitions if applicable

  1. Generate API contracts from functional requirements:

- For each user action → endpoint - Use standard REST/GraphQL patterns - Output OpenAPI/GraphQL schema to /contracts/

  1. Agent context update:

- Run `` - These scripts detect which AI agent is in use - Update the appropriate agent-specific context file - Add only new technology from current plan - Preserve manual additions between markers

Output: data-model.md, /contracts/*, quickstart.md, agent-specific file

Key rules

  • Use absolute paths
  • ERROR on gate failures or unresolved clarifications

⛔ MANDATORY STOP POINT

This command is COMPLETE after generating planning artifacts.

After reporting:

  • plan.md path
  • research.md path (if generated)
  • data-model.md path (if generated)
  • contracts/ contents (if generated)
  • Agent context file updated

YOU MUST STOP HERE.

Do NOT:

  • ❌ Generate tasks.md
  • ❌ Create work package (WP) files
  • ❌ Create tasks/ subdirectories
  • ❌ Proceed to implementation

The user will run /spec-kitty.tasks when they are ready to generate work packages.

Next suggested command: /spec-kitty.tasks (user must invoke this explicitly)

平台分布

Codex

32.2%
按下载量换算55

Claude

29.31%
按下载量换算50

Cursor

20.66%
按下载量换算35

Gemini CLI

9.88%
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

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

继续浏览同类 Skills