pi-setup: Design & UI for AI Agents | OpenAgentSkill

Overview

This skill should be used when the user wants to install or configure Pi Agent (@earendil-works/pi-coding-agent) with DeepSeek (built-in), Ant-Ling Ring-2.6-1T (single-model custom provider), and ZenMux (multi-model OpenAI-compatible aggregator including Gemini 3.5 Flash, inclusionAI Ling-3.0-flash, Claude, GPT), including auth, models.json, settings.json, a curated extension set, and known-pitfall fixes. Triggers on "配置 pi"、"setup pi agent"、"pi 装一下"、"配 ring/deepseek/gemini/zenmux/ling 到 pi".

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Pi Setup

帮用户从零配置 Pi Agent，搭配 DeepSeek V4（内置）+ Ant-Ling Ring-2.6-1T（单模型自定义 provider）+ ZenMux（多模型聚合 provider，含 Gemini 3.5 Flash、inclusionAI Ling-3.0-flash、Claude、GPT 等），装一套精选扩展，并自动规避 10 个已知坑。

用户输入的参数：$ARGUMENTS

核心原则

检测优先：每一步先探测现状（pi 是否已装、配置是否已存在、密钥在哪个 shell 文件），再决定动作。绝不盲目覆盖用户已有配置——发现已存在就 diff 给用户看，问是否覆盖。
密钥不落明文：pi 配置里只放 !shell命令 惰性读取，密钥留在用户原文件（~/.deepseek、shell rc 等）。
交互确认关键选择：用 AskUserQuestion 收集"启用哪些模型"等决策，不替用户拍板。
环境变量靠检测提醒：不假设是 ~/.zshrc，扫所有候选文件 + 当前进程环境，按实际情况给针对性提示。

工作流程

1. 检测现状

并行跑这些探测，汇总成一张"现状表"：

which pi && pi --version                       # pi 是否已装、版本
ls -la ~/.pi/agent/ 2>/dev/null                # 现有配置文件
cat ~/.pi/agent/settings.json 2>/dev/null      # 现有 settings（packages/默认模型）
cat ~/.pi/agent/models.json 2>/dev/null        # 现有自定义 provider
echo $SHELL                                    # 用户默认 shell
ls -la ~/.zenmux ~/.config/zenmux 2>/dev/null  # ZenMux 密钥文件候选

把"已装/未装、哪些配置已存在、默认 shell、ZenMux 密钥文件是否存在"整理给用户，再继续。

2. 环境变量检测（不限 ~/.zshrc）

这是本 skill 的重点。 不要假设密钥在 ~/.zshrc。按以下顺序探测每个所需变量（DEEPSEEK_API_KEY、LING_API_KEY、ZENMUX_API_KEY，以及用户额外要的）：

# a) 当前进程环境（最权威——说明已 export 且生效）
printenv DEEPSEEK_API_KEY >/dev/null && echo "DEEPSEEK_API_KEY: in live env"
printenv LING_API_KEY     >/dev/null && echo "LING_API_KEY: in live env"
printenv ZENMUX_API_KEY   >/dev/null && echo "ZENMUX_API_KEY: in live env"

# b) 独立密钥文件
[ -f ~/.deepseek ] && grep -l DEEPSEEK_API_KEY ~/.deepseek 2>/dev/null
for f in ~/.zenmux ~/.config/zenmux/key ~/.config/zenmux/api_key; do
  [ -f "$f" ] && grep -lE '^(ZENMUX_)?API_KEY=' "$f" 2>/dev/null
done

# c) 扫所有常见 shell 启动文件 + 是否带 export
for f in ~/.zshrc ~/.zshenv ~/.zprofile ~/.bashrc ~/.bash_profile ~/.profile ~/.config/fish/config.fish ~/.deepseek ~/.zenmux; do
  [ -f "$f" ] && grep -nE 'DEEPSEEK_API_KEY|LING_API_KEY|ZENMUX_API_KEY|^API_KEY=' "$f" 2>/dev/null | sed "s|^|$f:|"
done

ZenMux 密钥文件的常见两种格式：

~/.zenmux（或 ~/.config/zenmux/key）独立文件，内含 API_KEY=sk-... 或 ZENMUX_API_KEY=sk-...
环境变量 ZENMUX_API_KEY 在 shell rc 里 export

按检测结果分情况提醒（写进最终报告）：

检测结果	提醒动作
变量在 live env	✅ 可直接用 `apiKey: "VAR_NAME"`（最简单）
在某 rc 文件且带 `export`	✅ 新开 shell 生效；告知具体文件
在某 rc 文件但无 `export`（坑 1）	⚠️ 子进程读不到，用 `!{shell} -ic 'echo $VAR'` 惰性读，或建议加 `export`
在独立文件（如 `~/.deepseek`）	✅ 用 `!awk` / `!grep` 从该文件提取
完全找不到	❗ 明确告诉用户该把哪个变量加到哪个文件（用其默认 shell 对应的 rc），给出可粘贴的行

shell rc 选择按 $SHELL 推导：zsh→~/.zshrc，bash→~/.bashrc(交互)/~/.bash_profile(登录)，fish→~/.config/fish/config.fish。

3. AskUserQuestion — 收集关键决策

用 AskUserQuestion 一次性问以下问题（能合并成一次调用就合并）。AskUserQuestion 每问最多 4 个选项，Q1 已经顶到上限，所以 ZenMux 用单一捆绑选项加入（同时启用 paid + free 两个 Gemini 模型）。

Q1 — 启用哪些模型（multiSelect: true，引导用户全选）

deepseek-v4-pro（DeepSeek V4 Pro，1M ctx，内置）
deepseek-v4-flash（DeepSeek V4 Flash，1M ctx，内置，便宜）
ant-ling/Ring-2.6-1T（蚂蚁 Ring 2.6 1T，256K ctx，自定义）
zenmux/google/gemini-3.5-flash（ZenMux 聚合，Google 5月19日 GA，agentic 强；会同时启用 -free 限免版和 inclusionai/ling-3.0-flash（124B MoE / 5.1B active，262K ctx，非推理模型，便宜量大，适合当廉价执行模型）——三个模型共用同一个 provider 块，不占额外问答槽位；跨家对比 Claude/GPT 也走这个 provider，setup 后追加到 models.json 即可）
在 question 文案里写明"默认建议全选——它们都进 Ctrl+P 循环列表，不占额外上下文"

Q2 — 默认模型（单选，选项来自 Q1 已选项）

决定 settings.json 的 defaultProvider / defaultModel

Q3 — 默认 thinking 级别（单选）

xhigh（最强推理，Ring 烧 token 多但质量高）/ high（Gemini 3.5 Flash 默认推荐档）/ medium（通用） / off
提示：这是全局值，跨模型映射不同（DeepSeek 的 minimal/low/medium 不支持会被 clamp；Gemini 的 xhigh 在 thinkingLevelMap 里映射回 high；Ling-3.0-flash 是非推理模型，若默认模型选它则 defaultThinkingLevel 直接写 "off"）

Q4 — 扩展集（单选）

全量 22 个（功能 8 + TUI 9 + 行为 5，约 7.7k tokens）/ 仅核心（mcp-adapter、web-access、subagents、fff、context-prune）/ 跳过扩展

只问这些；不要追问 skill 类型之类的元问题——参数已明确。

4. 写配置文件

按用户选择生成。所有 JSON 模板见 references/config-templates.md，严格照抄（尤其 compat.supportsDeveloperRole: false 和 thinkingLevelMap，这是坑 2/3 的修复）。

写之前若文件已存在：读出来 diff 给用户，确认后再覆盖。

~/.pi/agent/auth.json → DeepSeek key（!awk 从 ~/.deepseek 或检测到的位置惰性读），chmod 600
~/.pi/agent/models.json → 把用户选中的自定义 provider 都写进 providers：
- 选了 Ring → ant-ling provider 块
- 选了 ZenMux → zenmux provider 块（默认同时包含 google/gemini-3.5-flash + google/gemini-3.5-flash-free + inclusionai/ling-3.0-flash 三个模型，apiKey 用 !awk 从检测到的 ZenMux 密钥文件惰性读；注意坑 9：自定义 provider 的 apiKey 写裸环境变量名会 403，必须用 !shell 惰性读形式）
- 两者都选 → 两个 provider 都写进同一个 providers 对象（合并，不要互相覆盖）
~/.pi/agent/settings.json → defaultProvider/Model、defaultThinkingLevel、enabledModels（用户在 Q1 选的；ZenMux 模型在 enabledModels 里用 zenmux/google/gemini-3.5-flash 形式）
DeepSeek 不写 models.json（内置，手配有害——见坑 7）
ZenMux Gemini 之外的模型（Claude / GPT 等）不在 skill 默认范围内自动写——会在最终报告里告诉用户"在 zenmux.models 数组追加即可，模型 ID 见 https://zenmux.ai"，避免硬编码不存在或已过期的 model ID

5. 装扩展

按 Q4 选择 pi install。命令清单见 references/config-templates.md 的"扩展安装"节。全局跳过 pi-autoresearch、@vanillagreen/pi-extension-manager、@vanillagreen/pi-session-manager。

6. 修扩展快捷键冲突（坑 6）

装了 @plannotator/pi-extension + @marckrenn/pi-sub-bar 时，写 ~/.pi/agent/pi-sub-bar-settings.json 把 cycleProvider 改 ctrl+alt+s（模板在 references）。

7. 验证

cat ~/.pi/agent/models.json | python3 -m json.tool >/dev/null && echo "models.json valid"
pi --list-models | grep -iE 'Ring|deepseek-v4|gemini|ling-3'
cd /tmp && pi --provider <默认provider> --model <默认model> -p "say pong" --no-extensions
pi -p "say hi" 2>&1 | grep -i conflict   # 无输出 = 快捷键冲突已解

若 Q1 选了 ZenMux Gemini，额外用 curl 验一次端点（避开 Pi 自身的扩展加载干扰）：

KEY=$(awk -F= '/^(ZENMUX_)?API_KEY=/{print $2; exit}' ~/.zenmux 2>/dev/null \
       || printenv ZENMUX_API_KEY)
curl -s -X POST https://zenmux.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
  -d '{"model":"google/gemini-3.5-flash-free","messages":[{"role":"user","content":"reply pong"}],"max_tokens":2048,"reasoning_effort":"low"}' \
  | python3 -c "import json,sys; r=json.load(sys.stdin); print('  content:', r['choices'][0]['message'].get('content','')[:80] or '(empty)')"

输出非空 = ZenMux 可达、key 有效、reasoning_effort 透传正常。空 content + finish_reason:length 多半是 max_tokens 给小了（Gemini 3.5 Flash 默认会先 reasoning，必须 ≥2K）。

Ling-3.0-flash 再补一发端到端 smoke（非推理模型，无需 reasoning_effort）：

cd /tmp && pi --provider zenmux --model inclusionai/ling-3.0-flash -p "reply with exactly: PONG" --no-extensions

任何一步 400 Invalid Request Messages、403 access_denied、截断或其它异常，先查 references/troubleshooting.md（10 个坑的现象→复现→根因→解法），按 curl 探针二分定位，再动手。

输出

完成后给用户一份报告：现状检测结果、环境变量提醒（按 §2 的表）、最终生成的文件清单、用户的模型/thinking 选择、验证结果、日常使用速查（Ctrl+L 切模型 / Shift+Tab 切 thinking / Ctrl+P 循环）。

配置文件直接落在 ~/.pi/agent/，不产出到 Downloads。

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File metadata

name: pi-setup
description: This skill should be used when the user wants to install or configure Pi Agent (@earendil-works/pi-coding-agent) with DeepSeek (built-in), Ant-Ling Ring-2.6-1T (single-model custom provider), and ZenMux (multi-model OpenAI-compatible aggregator including Gemini 3.5 Flash, inclusionAI Ling-3.0-flash, Claude, GPT), including auth, models.json, settings.json, a curated extension set, and known-pitfall fixes. Triggers on "配置 pi"、"setup pi agent"、"pi 装一下"、"配 ring/deepseek/gemini/zenmux/ling 到 pi".
license: MIT
allowed-tools: "Read,Write,Edit,Bash,AskUserQuestion"
version: "1.2.0"

View original text

---
name: pi-setup
description: This skill should be used when the user wants to install or configure Pi Agent (@earendil-works/pi-coding-agent) with DeepSeek (built-in), Ant-Ling Ring-2.6-1T (single-model custom provider), and ZenMux (multi-model OpenAI-compatible aggregator including Gemini 3.5 Flash, inclusionAI Ling-3.0-flash, Claude, GPT), including auth, models.json, settings.json, a curated extension set, and known-pitfall fixes. Triggers on "配置 pi"、"setup pi agent"、"pi 装一下"、"配 ring/deepseek/gemini/zenmux/ling 到 pi".
license: MIT
allowed-tools: "Read,Write,Edit,Bash,AskUserQuestion"
version: "1.2.0"
---

# Pi Setup

帮用户从零配置 Pi Agent，搭配 DeepSeek V4（内置）+ Ant-Ling Ring-2.6-1T（单模型自定义 provider）+ ZenMux（多模型聚合 provider，含 Gemini 3.5 Flash、inclusionAI Ling-3.0-flash、Claude、GPT 等），装一套精选扩展，并自动规避 10 个已知坑。

用户输入的参数：$ARGUMENTS

## 核心原则

1. **检测优先**：每一步先探测现状（pi 是否已装、配置是否已存在、密钥在哪个 shell 文件），再决定动作。绝不盲目覆盖用户已有配置——发现已存在就 diff 给用户看，问是否覆盖。
2. **密钥不落明文**：pi 配置里只放 `!shell命令` 惰性读取，密钥留在用户原文件（`~/.deepseek`、shell rc 等）。
3. **交互确认关键选择**：用 AskUserQuestion 收集"启用哪些模型"等决策，不替用户拍板。
4. **环境变量靠检测提醒**：不假设是 `~/.zshrc`，扫所有候选文件 + 当前进程环境，按实际情况给针对性提示。

## 工作流程

### 1. 检测现状

并行跑这些探测，汇总成一张"现状表"：

```bash
which pi && pi --version                       # pi 是否已装、版本
ls -la ~/.pi/agent/ 2>/dev/null                # 现有配置文件
cat ~/.pi/agent/settings.json 2>/dev/null      # 现有 settings（packages/默认模型）
cat ~/.pi/agent/models.json 2>/dev/null        # 现有自定义 provider
echo $SHELL                                    # 用户默认 shell
ls -la ~/.zenmux ~/.config/zenmux 2>/dev/null  # ZenMux 密钥文件候选
```

把"已装/未装、哪些配置已存在、默认 shell、ZenMux 密钥文件是否存在"整理给用户，再继续。

### 2. 环境变量检测（不限 ~/.zshrc）

**这是本 skill 的重点。** 不要假设密钥在 `~/.zshrc`。按以下顺序探测每个所需变量（`DEEPSEEK_API_KEY`、`LING_API_KEY`、`ZENMUX_API_KEY`，以及用户额外要的）：

```bash
# a) 当前进程环境（最权威——说明已 export 且生效）
printenv DEEPSEEK_API_KEY >/dev/null && echo "DEEPSEEK_API_KEY: in live env"
printenv LING_API_KEY     >/dev/null && echo "LING_API_KEY: in live env"
printenv ZENMUX_API_KEY   >/dev/null && echo "ZENMUX_API_KEY: in live env"

# b) 独立密钥文件
[ -f ~/.deepseek ] && grep -l DEEPSEEK_API_KEY ~/.deepseek 2>/dev/null
for f in ~/.zenmux ~/.config/zenmux/key ~/.config/zenmux/api_key; do
  [ -f "$f" ] && grep -lE '^(ZENMUX_)?API_KEY=' "$f" 2>/dev/null
done

# c) 扫所有常见 shell 启动文件 + 是否带 export
for f in ~/.zshrc ~/.zshenv ~/.zprofile ~/.bashrc ~/.bash_profile ~/.profile ~/.config/fish/config.fish ~/.deepseek ~/.zenmux; do
  [ -f "$f" ] && grep -nE 'DEEPSEEK_API_KEY|LING_API_KEY|ZENMUX_API_KEY|^API_KEY=' "$f" 2>/dev/null | sed "s|^|$f:|"
done
```

ZenMux 密钥文件的常见两种格式：
- `~/.zenmux`（或 `~/.config/zenmux/key`）独立文件，内含 `API_KEY=sk-...` 或 `ZENMUX_API_KEY=sk-...`
- 环境变量 `ZENMUX_API_KEY` 在 shell rc 里 export

按检测结果分情况提醒（写进最终报告）：

| 检测结果 | 提醒动作 |
|---|---|
| 变量在 live env | ✅ 可直接用 `apiKey: "VAR_NAME"`（最简单） |
| 在某 rc 文件且带 `export` | ✅ 新开 shell 生效；告知具体文件 |
| 在某 rc 文件但**无 `export`**（坑 1） | ⚠️ 子进程读不到，用 `!{shell} -ic 'echo $VAR'` 惰性读，或建议加 `export` |
| 在独立文件（如 `~/.deepseek`） | ✅ 用 `!awk` / `!grep` 从该文件提取 |
| 完全找不到 | ❗ 明确告诉用户该把哪个变量加到哪个文件（用其默认 shell 对应的 rc），给出可粘贴的行 |

shell rc 选择按 `$SHELL` 推导：zsh→`~/.zshrc`，bash→`~/.bashrc`(交互)/`~/.bash_profile`(登录)，fish→`~/.config/fish/config.fish`。

### 3. AskUserQuestion — 收集关键决策

用 **AskUserQuestion** 一次性问以下问题（能合并成一次调用就合并）。**AskUserQuestion 每问最多 4 个选项**，Q1 已经顶到上限，所以 ZenMux 用单一捆绑选项加入（同时启用 paid + free 两个 Gemini 模型）。

**Q1 — 启用哪些模型**（`multiSelect: true`，引导用户全选）
- `deepseek-v4-pro`（DeepSeek V4 Pro，1M ctx，内置）
- `deepseek-v4-flash`（DeepSeek V4 Flash，1M ctx，内置，便宜）
- `ant-ling/Ring-2.6-1T`（蚂蚁 Ring 2.6 1T，256K ctx，自定义）
- `zenmux/google/gemini-3.5-flash`（ZenMux 聚合，Google 5月19日 GA，agentic 强；**会同时启用 `-free` 限免版和 `inclusionai/ling-3.0-flash`（124B MoE / 5.1B active，262K ctx，非推理模型，便宜量大，适合当廉价执行模型）**——三个模型共用同一个 provider 块，不占额外问答槽位；跨家对比 Claude/GPT 也走这个 provider，setup 后追加到 models.json 即可）
- 在 question 文案里写明"默认建议全选——它们都进 Ctrl+P 循环列表，不占额外上下文"

**Q2 — 默认模型**（单选，选项来自 Q1 已选项）
- 决定 `settings.json` 的 `defaultProvider` / `defaultModel`

**Q3 — 默认 thinking 级别**（单选）
- `xhigh`（最强推理，Ring 烧 token 多但质量高）/ `high`（Gemini 3.5 Flash 默认推荐档）/ `medium`（通用） / `off`
- 提示：这是全局值，跨模型映射不同（DeepSeek 的 minimal/low/medium 不支持会被 clamp；Gemini 的 xhigh 在 thinkingLevelMap 里映射回 high；Ling-3.0-flash 是**非推理模型**，若默认模型选它则 `defaultThinkingLevel` 直接写 `"off"`）

**Q4 — 扩展集**（单选）
- 全量 22 个（功能 8 + TUI 9 + 行为 5，约 7.7k tokens）/ 仅核心（mcp-adapter、web-access、subagents、fff、context-prune）/ 跳过扩展

只问这些；不要追问 skill 类型之类的元问题——参数已明确。

### 4. 写配置文件

按用户选择生成。**所有 JSON 模板见** `references/config-templates.md`，**严格照抄**（尤其 `compat.supportsDeveloperRole: false` 和 `thinkingLevelMap`，这是坑 2/3 的修复）。

写之前若文件已存在：读出来 diff 给用户，确认后再覆盖。

- `~/.pi/agent/auth.json` → DeepSeek key（`!awk` 从 `~/.deepseek` 或检测到的位置惰性读），`chmod 600`
- `~/.pi/agent/models.json` → 把用户选中的自定义 provider 都写进 `providers`：
  - 选了 Ring → `ant-ling` provider 块
  - 选了 ZenMux → `zenmux` provider 块（**默认同时包含 `google/gemini-3.5-flash` + `google/gemini-3.5-flash-free` + `inclusionai/ling-3.0-flash` 三个模型**，apiKey 用 `!awk` 从检测到的 ZenMux 密钥文件惰性读；**注意坑 9：自定义 provider 的 `apiKey` 写裸环境变量名会 403，必须用 `!shell` 惰性读形式**）
  - 两者都选 → 两个 provider 都写进同一个 `providers` 对象（合并，不要互相覆盖）
- `~/.pi/agent/settings.json` → defaultProvider/Model、defaultThinkingLevel、enabledModels（用户在 Q1 选的；ZenMux 模型在 `enabledModels` 里用 `zenmux/google/gemini-3.5-flash` 形式）
- DeepSeek **不写 models.json**（内置，手配有害——见坑 7）
- ZenMux Gemini 之外的模型（Claude / GPT 等）**不在 skill 默认范围内自动写**——会在最终报告里告诉用户"在 `zenmux.models` 数组追加即可，模型 ID 见 https://zenmux.ai"，避免硬编码不存在或已过期的 model ID

### 5. 装扩展

按 Q4 选择 `pi install`。命令清单见 `references/config-templates.md` 的"扩展安装"节。全局跳过 `pi-autoresearch`、`@vanillagreen/pi-extension-manager`、`@vanillagreen/pi-session-manager`。

### 6. 修扩展快捷键冲突（坑 6）

装了 `@plannotator/pi-extension` + `@marckrenn/pi-sub-bar` 时，写 `~/.pi/agent/pi-sub-bar-settings.json` 把 cycleProvider 改 `ctrl+alt+s`（模板在 references）。

### 7. 验证

```bash
cat ~/.pi/agent/models.json | python3 -m json.tool >/dev/null && echo "models.json valid"
pi --list-models | grep -iE 'Ring|deepseek-v4|gemini|ling-3'
cd /tmp && pi --provider <默认provider> --model <默认model> -p "say pong" --no-extensions
pi -p "say hi" 2>&1 | grep -i conflict   # 无输出 = 快捷键冲突已解
```

若 Q1 选了 ZenMux Gemini，额外用 curl 验一次端点（避开 Pi 自身的扩展加载干扰）：

```bash
KEY=$(awk -F= '/^(ZENMUX_)?API_KEY=/{print $2; exit}' ~/.zenmux 2>/dev/null \
       || printenv ZENMUX_API_KEY)
curl -s -X POST https://zenmux.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $KEY" -H "Content-Type: application/json" \
  -d '{"model":"google/gemini-3.5-flash-free","messages":[{"role":"user","content":"reply pong"}],"max_tokens":2048,"reasoning_effort":"low"}' \
  | python3 -c "import json,sys; r=json.load(sys.stdin); print('  content:', r['choices'][0]['message'].get('content','')[:80] or '(empty)')"
```

输出非空 = ZenMux 可达、key 有效、reasoning_effort 透传正常。空 content + `finish_reason:length` 多半是 `max_tokens` 给小了（Gemini 3.5 Flash 默认会先 reasoning，必须 ≥2K）。

Ling-3.0-flash 再补一发端到端 smoke（非推理模型，无需 reasoning_effort）：

```bash
cd /tmp && pi --provider zenmux --model inclusionai/ling-3.0-flash -p "reply with exactly: PONG" --no-extensions
```

任何一步 `400 Invalid Request Messages`、`403 access_denied`、截断或其它异常，**先查** `references/troubleshooting.md`（10 个坑的现象→复现→根因→解法），按 curl 探针二分定位，再动手。

## 输出

完成后给用户一份报告：现状检测结果、环境变量提醒（按 §2 的表）、最终生成的文件清单、用户的模型/thinking 选择、验证结果、日常使用速查（Ctrl+L 切模型 / Shift+Tab 切 thinking / Ctrl+P 循环）。

配置文件直接落在 `~/.pi/agent/`，不产出到 Downloads。

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
README.md states version 1.1.0 while SKILL.md and CHANGELOG.md indicate 1.2.0; minor inconsistency.
The skill relies on `!shell` commands for lazy key reading; edge cases in quoting or file paths could cause failures, but these are documented.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 88 GitHub stars
Stars/forks activity: 88 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1Read the source. Confirm the input, expected output, dependencies and permissions.
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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: wquguru/skills
License: MIT
Version: 1.2.0
Last GitHub push: Aug 5, 2026
Registry updated: Sep 7, 2026
Instruction path: skills/pi-setup/SKILL.md @ ceb6f66e3635

Version reported in registry metadata; check source releases before relying on it.

Quality

61/100

Promising

Trust

55/100

Do not auto-install

Agent access

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Open install API LLM text format Open manifest Auto-resolve Plan

More details

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    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
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    "The skill relies on `!shell` commands for lazy key reading; edge cases in quoting or file paths could cause failures, but these are documented."
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      "Safety: 35/100 Avoid automatic install",
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}

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Creator: wquguru
Source: wquguru/skills
Indexed by: OpenAgentSkill community index

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