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/yezannnnn/agentgroup/suggestgit clone --depth 1 https://github.com/yezannnnn/agentGroupWrote 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/yezannnnn/agentgroup/suggest)<a href="https://agentmods.dev/commands/yezannnnn/agentgroup/suggest"><img src="https://agentmods.dev/badge/commands/yezannnnn/agentgroup/suggest.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 | $0.00540 |
| Opus 5 | $0.00000 | $0.00270 |
| Sonnet 5 | $0.00000 | $0.00108 |
| Haiku 4.5 | $0.00000 | $0.00054 |
Grade A, and why
suggest 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 5d 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
/suggest - 产品建议
提供产品方向和优先级建议
使用方式
/suggest [需求或问题描述]
/suggest priority [功能列表]
/suggest review [PRD文件]
需求评估
# 需求评估 - [需求名称]
## 需求概述
[简要描述]
## 评估维度
### 用户价值 ⭐⭐⭐⭐⭐
[分析用户价值]
### 业务价值 ⭐⭐⭐⭐☆
[分析业务价值]
### 实现成本 ⭐⭐⭐☆☆
[评估开发成本]
### 风险评估 ⭐⭐☆☆☆
[识别潜在风险]
## 建议
**优先级**: P0/P1/P2/P3
**建议**: [是否做/如何做/替代方案]
**理由**: [决策依据]
优先级排序
# 功能优先级排序
## 评估矩阵
| 功能 | 用户价值 | 业务价值 | 成本 | 总分 | 优先级 |
|------|----------|----------|------|------|--------|
| [功能A] | 5 | 4 | 2 | 7 | P0 |
| [功能B] | 3 | 3 | 4 | 2 | P2 |
评分规则:
- 用户价值: 1-5分
- 业务价值: 1-5分
- 成本: 1-5分 (越低越好)
- 总分 = 用户价值 + 业务价值 - 成本
## 建议排期
### 第一期 (P0)
1. [功能A] - 核心功能，必须做
### 第二期 (P1)
1. [功能C] - 重要功能，优先做
### 后续迭代 (P2+)
1. [功能B] - 可以延后
PRD评审
# PRD评审 - [文档名]
## 评审结论: ✅ 通过 / ⚠️ 需修改 / ❌ 不通过
## 优点
1. [值得肯定的地方]
## 问题与建议
### 问题1: [问题描述]
**影响**: [影响范围]
**建议**: [修改建议]
### 问题2: [问题描述]
...
## 遗漏项
- [ ] [需要补充的内容]
## 下一步
[建议的后续行动]
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.
- 5d ago First seen · 96 lines · 0 tokens per session scan A 0f5ea63b52a6
suggest is a command published in the GitHub repository yezannnnn/agentGroup (149 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 540 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
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.