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 kennyzir/7deer_skills --skill backlink-discoverygit clone --depth 1 https://github.com/kennyzir/7deer_skillsWrote 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/kennyzir/7deer_skills/backlink-discovery)<a href="https://agentmods.dev/skills/kennyzir/7deer_skills/backlink-discovery"><img src="https://agentmods.dev/badge/skills/kennyzir/7deer_skills/backlink-discovery/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/kennyzir/7deer_skills/backlink-discovery"><img src="https://agentmods.dev/badge/skills/kennyzir/7deer_skills/backlink-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.01248 |
| Opus 5 | $0.00049 | $0.00624 |
| Sonnet 5 | $0.00020 | $0.00250 |
| Haiku 4.5 | $0.00010 | $0.00125 |
Grade A, and why
backlink-discovery 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backlink Discovery — 外链机会发现引擎
核心逻辑
每个 URL = 独立发现任务，目标 300 个平台。
用户输入 https://xxx.com
→ 立即用 web_search 开始前台搜索
→ 多轮关键词派生，持续发现
→ 达到 300 个平台后停止
→ 结果存入数据库
下次输入 https://yyy.com
→ 新的独立发现任务
→ 各自数据库，互不干扰
触发方式
发现 https://xxx.com 的外链机会（目标300个）
执行流程（前台 web_search）
Step 1：接收任务，提取种子关键词
解析目标 URL，实时提取种子关键词（不写死）：
https://robloxcalc.com
↓
种子关键词：roblox, calculator, math, lua, game, game calculator, gaming, education, tool
Step 2：多轮 web_search（前台执行）
每轮用 web_search 搜索 8-12 个查询，从结果提取平台域名 → 分类 → 判断相关度。
搜索词思路要宽，不要只搜"xxx alternatives"：
- 竞品 + "free tools list"
- 竞品 + "directory submit"
- 竞品 + "community"
- 竞品 + "tutorial resources"
- 竞品 + "wiki"
- 竞品 + "github awesome list"
- 竞品 + "for kids/math/education"
- 竞品 + "game dev"
- 竞品 + "fan sites blogs"
- 竞品 + "similar games"
- 竞品 + "submit free"
每轮结束后从发现结果中派生新关键词 → 进入下一轮。
停止条件：
- 达到 300 个平台 → 停止
- 连续 2 轮无新发现 → 停止
Step 3：结果写入数据库
每个 URL 独立数据库目录：
memory/backlink-discovery/{提取的域名}/
├── platforms.json # 所有发现平台（含相关度、方式分类）
└── summary.md # 汇总报告
Step 4：返回结果
发现完成后返回：
- 总平台数 + P0/P1/P2 分布
- 外链方式分布
- Top 10 高相关平台
- 数据存放路径
用户查询时读数据库返回最新状态。
数据库读取
查询 https://xxx.com 的外链发现结果
→ 读 memory/backlink-discovery/xxx_com/platforms.json
→ 返回 P0/P1/P2 分类 + 外链方式分布 + 完成状态
平台分类体系
| 分类 | 相关度 | 说明 |
|---|---|---|
| 🔴 P0 | ≥0.7 | 主题高度相关，明显外链机会 |
| 🟡 P1 | 0.4-0.7 | 较相关，可尝试 |
| 🟢 P2 | <0.4 | 关联一般 |
外链方式分类
| 方式 | 适用平台 |
|---|---|
| forum_post | Reddit、Discord、GameFAQs 等社区 |
| github_pr | GitHub Awesome Lists、资源列表 |
| wiki_edit | Wikipedia、Fandom Wiki |
| alternatives_article | Alternatives/对比文章站 |
| directory_submit | 目录站、产品提交页 |
| guest_post | Dev.to、Hashnode、技术博客 |
| email_outreach | 有联系邮箱的任意平台 |
| social_post | Twitter、LinkedIn |
| manual_review | 其他方式 |
输出格式
# {网站} 外链机会发现报告
## 发现状态
- 状态：已完成
- 平台库：X / 300 个
- 发现时间：时间
## 🔴 P0 高相关平台（X 个）
[表格]
## 🟡 P1 中相关平台（X 个）
[按类型分组列出]
## 🟢 P2 低相关平台（X 个）
## 📊 外链方式分布
- Forum 发帖：X 个
- GitHub PR：X 个
- Alternatives Outreach：X 个
- 目录提交：X 个
- 其他：X 个
## 🔄 下一步
建议执行 backlink-executor 处理 P0 平台
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 157 lines · 98 tokens per session scan A 4b6423a495f4
backlink-discovery is a skill published in the GitHub repository kennyzir/7deer_skills (313 stars, last pushed 4d ago), licensed MIT. It adds 98 tokens to every session and 1,248 once invoked, about $0.0005 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 2026-08-30.
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