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 skills add AgiWish/hermes-skills-zh --skill competitive-analysis-zhgit clone --depth 1 https://github.com/AgiWish/hermes-skills-zhWrote 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/skills/agiwish/hermes-skills-zh/competitive-analysis-zh)<a href="https://agentmods.dev/skills/agiwish/hermes-skills-zh/competitive-analysis-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/competitive-analysis-zh/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agiwish/hermes-skills-zh/competitive-analysis-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/competitive-analysis-zh.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00041 | $0.00821 |
| Opus 5 | $0.00020 | $0.00411 |
| Sonnet 5 | $0.00008 | $0.00164 |
| Haiku 4.5 | $0.00004 | $0.00082 |
Grade A, and why
competitive-analysis-zh 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 12d 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
竞品分析 (competitive-analysis-zh)
When to Use
- "帮我分析一下竞品"、"做个竞品对比"
- "我们和 [产品名] 比有什么优劣势"
- 立项前需要市场调研
/competitive-analysis-zh [你的产品] vs [竞品1, 竞品2]
Quick Reference
/competitive-analysis-zh [产品描述或名称]
可选参数：
--focus=功能 # 重点对比功能矩阵（默认）
--focus=商业 # 重点对比商业模式 / 定价
--focus=用户 # 重点对比用户定位 / 体验
Procedure
-
确认分析范围
- 你的产品定位 / 核心功能是什么？
- 主要竞品是哪几个（直接竞品 + 间接竞品）？
- 分析目的：立项决策 / 功能规划 / 融资材料？
-
输出竞品分析报告
## 一、市场概况
- 赛道定义：[这个市场解决什么问题]
- 市场规模：[已知数据，无则标注"待调研"]
- 竞争格局：[第一梯队 / 细分玩家]
## 二、竞品概览
| 产品 | 核心定位 | 目标用户 | 商业模式 | 融资情况 |
|------|----------|----------|----------|----------|
| [我方] | | | | |
| [竞品A] | | | | |
| [竞品B] | | | | |
## 三、功能矩阵对比
| 功能点 | 我方 | 竞品A | 竞品B | 重要性 |
|--------|------|-------|-------|--------|
| [核心功能1] | ✅ | ✅ | ❌ | 高 |
| [核心功能2] | ⚠️部分 | ✅ | ✅ | 中 |
图例：✅ 完整支持 / ⚠️ 部分支持 / ❌ 不支持
## 四、差异化分析
- **我方核心优势**：[2-3 个真实差异点]
- **明显短板**：[诚实列出，不要回避]
- **竞品值得借鉴的点**：[学什么]
## 五、策略建议
- 短期（0-3个月）：[可执行的具体行动]
- 中期（3-12个月）：[差异化深化方向]
- 规避：[不要正面硬碰的方向]
- 数据来源标注
- 已知事实打 ✓，推断内容标注
[推断]，需验证的标注[待核实]
- 已知事实打 ✓，推断内容标注
Pitfalls
- 不要只列功能有无，要说明实现深度和体验质量
- 差异化优势要真实，不要自我美化
- 如果对竞品不了解，明确告知哪些是推断，不要编造
Verification
- 竞品覆盖直接竞品和间接竞品
- 功能矩阵有明确的重要性权重
- 差异化分析有具体证据支撑
- 策略建议可落地，不是套话
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.
- 12d ago First seen · 89 lines · 41 tokens per session scan A d0fbb143efed
competitive-analysis-zh is a skill published in the GitHub repository AgiWish/hermes-skills-zh (5 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 821 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-31.
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