Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/dsgwjq/feagent/gemini-uigit clone --depth 1 https://github.com/DSGWJQ/FeagentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/dsgwjq/feagent/gemini-ui)<a href="https://agentmods.dev/commands/dsgwjq/feagent/gemini-ui"><img src="https://agentmods.dev/badge/commands/dsgwjq/feagent/gemini-ui.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00625 |
| Opus 5 | $0.00000 | $0.00313 |
| Sonnet 5 | $0.00000 | $0.00125 |
| Haiku 4.5 | $0.00000 | $0.00063 |
Grade A, and why
gemini-UI scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Core Instruction for Gemini MCP
在任何时刻，你必须思考当前过程可以如何与 gemini 进行协作，如何调用 gemini 为你提供的MCP工具作为你客观全面分析的保障。
其中你务必执行以下几个步骤：
1 在你对用户需求形成初步分析后，
（1）首先将用户原始需求（一字不差，严禁转述）、以及你的初始思路告知gemini；
（2）与gemini进行充分讨论、辩驳，以完善需求分析和实施计划。这一步的结束标志为，必须确保对用户需求的透彻理解，并生成切实可行的行动计划。
2 在实施具体编码任务前，你必须向gemini索要代码实现原型（要求gemini仅给出unified diff patch，严禁对代码做任何真实修改）。在获取代码原型后，你只能以此为逻辑参考，再次对代码修改进行重写，形成企业生产级别、可读性极高、可维护性极高的代码后，才能实施具体编程修改任务。
-
注意： Gemini 十分擅长前端代码，并精通样式、UI组件设计。在涉及前端代码时，你必须向其索要代码原型（CSS/React/Vue/HTML等前端代码），任何时刻，你必须以gemini的前端设计（原型代码）为最终的前端代码基点。
-
例如，当你识别到用户给出了前端设计需求，你的首要行为必须自动调整为，将用户需求原封不动转发给gemini，并让其出具代码示例（此阶段严禁对用户需求进行任何改动、简写等等）。即你必须从gemini获取代码基点，才可以进行接下来的各种行为。
Gemini Tool Invocation Specification
- 工具概述
gemini MCP 提供了一个工具 gemini，用于调用 Google Gemini 模型执行 AI 任务。该工具拥有极强的前端审美、任务规划与需求理解能力，但在**上下文长度（Effective 32k）**上有限制。
- 使用方式与规范
必须遵守的限制：
-
会话管理：捕获返回的
SESSION_ID用于多轮对话。 -
后端避让：严禁让 Gemini 编写复杂的后端业务逻辑代码。
擅长场景（必须优先调用 Gemini）：
-
需求清晰化：在任务开始阶段辅助生成引导性问题。
-
任务规划：生成 Step-by-step 的实施计划。
-
前端原型：编写 CSS、HTML、UI 组件代码，调整样式风格。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 40 lines · 0 tokens per session scan A 3a5f232ecced
gemini-UI is a command published in the GitHub repository DSGWJQ/Feagent (139 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 625 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.