MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
将用户的数据分析需求转化为结构化的分析计划并与用户确认。用户提出 BI 业务分析、数据探索、统计建模等需要规划的分析任务时使用。简单数据查询、公式明确的定量计算等流程简单、无需规划的任务不使用。
Apache-2.0Auto-check passed
Install Analysis Plan Builder
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builder --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .claude/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .claude/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builderType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builder --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .agents/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .agents/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .agents/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builder --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .cursor/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .cursor/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/agentscope-ai/QwenPaw-Data.git --path packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builder --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .gemini/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .gemini/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builderInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .github/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .github/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .github/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agentscope-ai/QwenPaw-Data analysis-plan-builder --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agentscope-ai/QwenPaw-Data.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder .opencode/skills/analysis-plan-builder && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analysis-plan-builder" agent skill from https://github.com/agentscope-ai/QwenPaw-Data/tree/main/packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder into .opencode/skills/analysis-plan-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-plan-builder", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
analysis-plan-builder- GitHub stars
- 113
- Token cost
- ~1.3k tokens
- SKILL.md length
- 252 words
- Files
- 12 (incl. references)
- Skills in repo
- 26
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
将用户的数据分析需求转化为结构化的分析计划并与用户确认。用户提出 BI 业务分析、数据探索、统计建模等需要规划的分析任务时使用。简单数据查询、公式明确的定量计算等流程简单、无需规划的任务不使用。
- Works in 2 steps: 上下文构建（Phase 1 — Context Gathering） → 生成分析计划
- SKILL.md covers 设计原则：行动优先, 前置条件, Step 1: 上下文构建（Phase 1 —… and Step 2: 生成分析计划, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Analysis Plan Builder is an agent skill from agentscope-ai/QwenPaw-Data. 将用户的数据分析需求转化为结构化的分析计划并与用户确认。用户提出 BI 业务分析、数据探索、统计建模等需要规划的分析任务时使用。简单数据查询、公式明确的定量计算等流程简单、无需规划的任务不使用。
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including reference files (for example `references/bi-business-type-guide.md`, `references/bi-metric-specification.md` and `references/metrics/tob.md`).
It works with Model Context Protocol. The repository describes itself as: Agentic enterprise data analytics: governed facts (DataBridge), reusable methodology (Skill-Hub), and controllable execution (Host). The licence is Apache-2.0.
Example prompts
- “/analysis-plan-builder”
Requirements
- Python 3
Workflow steps
2 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit e0bae36. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Analysis Plan Builder loads about 1.3k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 30 tokens; SKILL.md has 252 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from agentscope-ai/QwenPaw-Data at commit e0bae36, republished under its Apache-2.0 licence (© agentscope-ai). 252 words, ~1,292 tokens.
Download SKILL.mdSave it as
.claude/skills/analysis-plan-builder/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.- name
- analysis-plan-builder
- description
- 将用户的数据分析需求转化为结构化的分析计划并与用户确认。用户提出 BI 业务分析、数据探索、统计建模等需要规划的分析任务时使用。简单数据查询、公式明确的定量计算等流程简单、无需规划的任务不使用。
analysis-plan-builder
设计原则：行动优先
用户发出分析请求后最差的体验是"agent 一连串提问、什么都没做"。本 skill 要求：先收集上下文，再规划，最后根据信息充分性决定立即执行还是反问。 用户能看到信息不断收敛、分析在推进，同时保留随时打断纠偏的能力。
前置条件
开始规划前，确认以下信息已就绪：
- 用户问题的任务类型（如 BI 业务分析、数据探索、统计建模等）
Step 1: 上下文构建（Phase 1 — Context Gathering）
收集和补充构造分析计划所需的信息。本步骤应尽早执行，在向用户提问之前先通过工具获取尽可能多的上下文。
1.1 可用资源确认
确认当前可用的资源：
- 用户提供的数据文件或表
- 数据获取工具 / API
- 静态的域知识包
- 可获取业务领域模型的接口（后文称"语义层"），即能查询指标、维度及其关系的服务或工具
如果工具列表中包含 MCP 语义层工具（search_context / get_domain_overview / list_metrics 等），立即调用以获取域上下文：
- 推荐首个调用：
search_context(query=用户原始问题, domain=识别出的业务域)— 一次调用返回匹配的指标、口径、数据集和相关维度。 - 补充调用
get_domain_overview(domain)了解域全貌（当 search_context 返回不足时）。
实时向用户同步发现：将 MCP 返回的关键信息（指标名、口径定义、可用维度、数据表）以简洁文本输出到 assistant 消息。用户可随时打断纠偏。
如果没有 MCP 工具可用 → 跳过语义层调用，直接进入 1.2。
1.2 按任务类型补充上下文
- BI 业务分析 → 继续 1.3 ~ 1.5
- 其他任务类型（数据探索、统计建模等）→ 继续 1.6
BI 业务分析
若用户已明确指定了分析的指标（如"分析 DAU 变化"），可跳过 1.3 和 1.4，直接到 1.5 确认指标角色。
1.3 业务上下文识别
判定业务类型（ToB / ToC / ToD / Mixed）和业务子类型（如ToC 下的工具类 / 内容社区 / 电商 / 游戏），判定依据参考 references/bi-business-type-guide.md
1.4 分析范围确认
- 根据业务类型和子类型，读取
references/modules下对应的分析目录文件：
| 业务类型 + 子类型 | 文件 |
|---|---|
| ToC 工具类 | toc-tool.md |
| ToC 内容社区类 | toc-content.md |
| ToC 电商交易类 | toc-ecommerce.md |
| ToC 游戏类 | toc-game.md |
| ToB 产品类 | tob-product.md |
| ToD 开发者社区 | tod-developer.md |
- 根据用户问题匹配具体的分析模块和分析内容：
- 命中分析内容条目（如"用户留存"）→ 仅匹配该条目
- 命中分析模块（如"用户趋势与规模"）→ 匹配该模块全量条目
- 语义意图匹配（如"增长分析"）→ 匹配相关模块的相关条目
- 宽泛问题（如"分析 3 月数据"）→ 匹配全量模块和条目
1.5 指标细化
根据匹配的分析内容条目，参照 references/bi-metric-specification.md，确定每个条目涉及的具体指标。
非 BI 任务（数据探索、统计建模等）
1.6 数据探查
对用户提供的数据进行初步探查，建立对数据的基本理解：
- 读取数据 schema（字段名、类型、数量）
- 查看数据规模（行数、时间跨度等）
- 采样查看数据内容，识别数据特征（缺失值、异常值、分布形态等）
- 结合用户问题，判断哪些字段/特征与分析目标相关
Step 2: 生成分析计划
将 Step 1 的结果组织为结构化的分析计划。
BI 业务分析的计划
包含以下内容：
- 意图：目标、分析对象、问题类型
- 已知条件：时间范围、数据来源、用户提到的指标/维度、约束条件
- 额外上下文：可用数据接口（取数或者语义层工具/API）、域知识约束（如有）
- 缺失信息：已识别但未解决的信息缺口
- 分析内容：每个分析模块的名称、分析条目、指标（名称、角色）
示例 — 用户："分析 产品A 3 月的访问趋势和对话情况"
task_type: bi_analysis
intent:
goal: "分析访问趋势和对话情况"
subject: "产品A"
question_type: what
known_conditions:
period: { start: "2026-03-01", end: "2026-03-31" }
metric_mentions: ["访问用户数", "对话次数"]
context:
business: { type: "ToC", sub_type: "工具类", domain: "product_a" }
data: { semantic_layer: available, apis: ["semantic-layer", "fetch-data"] }
domain_constraints: ["率值变动使用 pt 表达", "归因贡献度正/负各取 Top 3"]
scope:
modules:
- module_name: "用户趋势与规模"
analysis_items: ["用户规模", "增长趋势", "增长归因"]
metrics:
- name: "访问用户数"
roles: [north_star]
- name: "新增用户数"
roles: [display]
- module_name: "使用行为及体验"
analysis_items: ["功能使用分析"]
metrics:
- name: "人均对话次数"
roles: [north_star]
- name: "对话次数"
roles: [display]非 BI 任务的计划
非 BI 任务没有固定的模块体系，需要根据用户问题与给定数据明确数据范围、条件约束以及识别未解决的信息缺口等前置信息，随后将用户问题分解为一系列可执行的原子子任务。每个子任务应当是具体的、可执行的操作（数据清洗、计算、检验、可视化等），而非笼统的分析方向。
未解决的信息缺口主要关注：
- 数据范围的歧义。例如，无法从问题和数据模式中准确推断出需要分析的数据子集。
- 语义的歧义。例如，数据中包含的数据列的含义不明确。
问题分解需要依照以下原则：
- 子任务必须包含可执行的数据检查、数据转换或计算操作；
- 子任务能够通过使用数据分析或数据科学库（如 pandas、numpy、scipy）执行 Python 代码来实现；
- 子任务考虑必要的数据清洗和预处理步骤（例如，缺失值的预处理/插补）；
最终，生成结构化的分析计划，计划包含：
- 意图：目标、分析对象、现象、问题类型
- 已知条件：数据来源、约束条件
- 数据概况：数据探查发现的关键信息（schema、数据量、数据质量等）
- 子任务序列：按执行顺序排列的原子任务，需考虑：
- 必要的数据清洗和预处理（缺失值处理、类型转换、异常值过滤等）
- 具体的计算或变换（分组统计、分布计算、相关性分析等）
- 可执行的检验或判断（阈值检查、统计检验等）
- 缺失信息：已识别但未解决的信息缺口
示例 — 用户："帮我看看 user_behavior.csv，为什么 age 列分布这么不均匀？"
task_type: data_exploration
intent:
goal: "分析 age 列分布不均的原因"
subject: "user_behavior.csv 的 age 列"
phenomenon: "分布不均"
question_type: why
known_conditions:
data_source: "user_behavior.csv"
data_profile:
rows: 50000
columns: 12
age_column: { type: int, missing_rate: 3.2%, range: "0-120", median: 28 }
tasks:
- "过滤 age 列的缺失值和明显异常值（<=0 或 >120），记录过滤比例"
- "计算 age 列的分布直方图（bin=5），识别峰值和异常聚集区间"
- "按 source 字段分组，分别计算各渠道的 age 均值和分布，对比差异"
- "计算 age 与 registration_date 的相关性，判断是否存在注册批次效应"
- "对分布不均的区间（如 age=0 或 age>100），抽样查看原始记录，判断数据质量问题"
missing_info:
- "不清楚 age 列的业务含义（用户年龄？账号年龄？）"产出
Step 2 的 YAML 即本 skill 的最终产出，作为 plan 草稿交付给 host。
信息充分性检查（强制）
交付 plan 草稿前，检查 missing_info 字段：
missing_info为空 → 信息充分，可以进入create_planmissing_info不为空 → 缺少关键信息，不应进入create_plan，应先向用户反问缺失项（参照interaction-strategyskill 的反问格式）
注意：当且仅当 missing_info 中的缺失项无法通过工具自行获取时才反问。如果 MCP 语义层工具可以消歧，应先调用工具解决，而不是停下来问用户。
Phase 3 — create_plan 后的行为（强制）
create_plan 完成后，根据 interaction-strategy skill 的 Type 2 触发条件判断：
- Type 2 未触发（默认情况）→ 输出 1-2 句 plan 概览，立即调用
update_subtask开始执行第一个 ready 节点。不等待用户确认。 - Type 2 触发 → 输出详细计划 + 确认语，不调用工具，等待用户回复。
进入执行阶段后，还需 read_file skills/runtime-guide/SKILL.md 获取执行期通用策略（复用、异常处理、计划调整、质量自检、执行节奏、产出策略等）。
© agentscope-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 11 other files (references) in packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder of agentscope-ai/QwenPaw-Data.
- SKILL.md
- references/bi-business-type-guide.md
- references/bi-metric-specification.md
- references/metrics/tob.md
- references/metrics/toc.md
- references/metrics/tod.md
- references/modules/tob-product.md
- references/modules/toc-content.md
- references/modules/toc-ecommerce.md
- references/modules/toc-game.md
- references/modules/toc-tool.md
- references/modules/tod-developer.md
Open the folder on GitHubat commit e0bae36
Compare with similar skills
Analysis Plan Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Analysis Plan Builder this skillagentscope-ai/QwenPaw-Data | 113 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 38k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Figma use_figma Plugin API Ruleswarpdotdev/warp | 65k | 4 repos | ~4.4k | Automated safety check: Pass | AGPL-3.0 | |
| Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills | 8.4k | 6 repos | ~3.2k | Automated safety check: Notes | Apache-2.0 |
Similar skills
- Official
MCP Server Builder
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
- Official
MCP Integration for Plugins
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
Figma use_figma Plugin API Rules
warpdotdev/warp
Required groundwork before any use_figma call: the rules and reference files for running JavaScript in a Figma file through the Plugin API without common failures.
- Official
Stitch to Remotion Walkthrough Videos
google-labs-code/stitch-skills
Builds walkthrough videos from Stitch design projects using Remotion, with transitions, zoom effects and text overlays on each screen.
MCP Development
coollabsio/coolify
A skill your agent uses for Laravel MCP development. An agent skill from coollabsio/coolify.
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Related
Works with
Questions about Analysis Plan Builder
What does Analysis Plan Builder do?
将用户的数据分析需求转化为结构化的分析计划并与用户确认。用户提出 BI 业务分析、数据探索、统计建模等需要规划的分析任务时使用。简单数据查询、公式明确的定量计算等流程简单、无需规划的任务不使用。. Analysis Plan Builder is an agent skill from agentscope-ai/QwenPaw-Data.
How do I install Analysis Plan Builder in Claude Code?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a claude-code`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder in agentscope-ai/QwenPaw-Data) into .claude/skills/analysis-plan-builder in your project. Claude Code loads it when a task matches its description.
How do I install Analysis Plan Builder in Codex?
Run `npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a codex`. Or copy the skill folder (packages/qwenpaw-data-skills/skills/planning/analysis-plan-builder in agentscope-ai/QwenPaw-Data) into .agents/skills/analysis-plan-builder in your project. Codex loads it when a task matches its description.
Can I use Analysis Plan Builder in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add agentscope-ai/QwenPaw-Data --skill analysis-plan-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analysis-plan-builder, .gemini/skills/analysis-plan-builder, .github/skills/analysis-plan-builder and .opencode/skills/analysis-plan-builder in your project.
What does Analysis Plan Builder need to run?
SKILL.md names no scripts, command-line tools or credentials: Analysis Plan Builder is instructions for the agent only. Our summary lists: Python 3.
Does Analysis Plan Builder access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Analysis Plan Builder safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Analysis Plan Builder use?
Analysis Plan Builder is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Analysis Plan Builder use?
About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4k tokens, read only when the agent opens those files.
What are the alternatives to Analysis Plan Builder?
Skills that share tags, products or a category with Analysis Plan Builder: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Analysis Plan Builder?
agentscope-ai (a GitHub organization) maintains it in agentscope-ai/QwenPaw-Data, which has 113 GitHub stars. The repository holds 26 skills in this directory. The repository was last updated on October 5, 2026.
Source: agentscope-ai/QwenPaw-Data on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.