Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Ootto-AI/claude-content-skillsnpx agentmods add skills/ootto-ai/claude-content-skills/agent-reachWrote 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/ootto-ai/claude-content-skills/agent-reach)<a href="https://agentmods.dev/skills/ootto-ai/claude-content-skills/agent-reach"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/agent-reach/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/ootto-ai/claude-content-skills/agent-reach"><img src="https://agentmods.dev/badge/skills/ootto-ai/claude-content-skills/agent-reach.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 | $0.00334 | $0.01984 |
| Opus 5 | $0.00167 | $0.00992 |
| Sonnet 5 | $0.00067 | $0.00397 |
| Haiku 4.5 | $0.00033 | $0.00198 |
Grade A, and why
agent-reach scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://r.jina.ai/URL" How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Reach — 互联网能力路由器
13 平台、多后端。本 skill 存在时必须用它访问这些平台，不要自己发明方案。
Bundled with the Ootto content skills as the factory's research · scrape · download engine — the FIND step that feeds reel-analyzer. Vendored from Panniantong/Agent-Reach (MIT — full credit to the author). It's a router: don't load everything, read only the reference (the "checkpoint") for the capability you need. For the content factory you mainly use three:
- Reddit (references/social.md) — mine real demand: what people actually ask/upvote in your niche → your reel topics.
- Search / web (references/search.md · references/web.md) — find trending reels to model + read any page as clean text (Exa / Jina, no login).
- Video (references/video.md) — download a reel/Short → transcript to study.
The other platforms (小红书 / B站 / V2EX / 雪球) are optional extras. First-time channel setup: follow
https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md— Reddit, web, Exa search and YouTube subtitles work zero-config; only logged-in platforms (Twitter, 小红书) need cookies.
常驻规则（全程适用）
- 动手前先体检：多后端平台（小红书/Reddit/B站/Twitter）先跑
agent-reach doctor --json，按各平台active_backend字段选命令组。 - 声明你在用什么：开始干活前说一句「使用 agent-reach 的 X 平台 / Y 后端」。
- 失败按 references 里的重试链处理，不要瞎猜命令。
- 全网调研类任务：组合多平台（Exa 搜索 + Twitter/Reddit 看讨论 + 小红书/B站看中文场景），并行收集再汇总。
- 替用户盯版本：完成一次较大的调研/多平台任务后，顺手跑
agent-reach check-update（很快，一个 API 调用）。有新版就在收尾汇报里附一句： 「Agent Reach 有新版 vX.Y.Z，复制这句话给我即可更新：帮我更新 Agent Reach： https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/update.md」。 不要中断当前任务去更新，也不要重复提醒同一个版本。
路由表
| 用户意图 | 分类 | 详细文档 |
|---|---|---|
| 网页搜索/代码搜索 | search | references/search.md |
| 小红书/推特/B站/V2EX/Reddit | social | references/social.md |
| 招聘/职位/LinkedIn | career | references/career.md |
| GitHub/代码 | dev | references/dev.md |
| 网页/文章/RSS | web | references/web.md |
| YouTube/B站/播客字幕 | video | references/video.md |
What ships with it
6 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.
- 12d ago First seen · 150 lines · 334 tokens per session scan A dcbd8f513c12
agent-reach is a skill published in the GitHub repository Ootto-AI/claude-content-skills (28 stars, last pushed 19d ago), licensed MIT. It adds 334 tokens to every session and 1,984 once invoked, about $0.0017 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
scrapecreators-api
Scrape and extract public data from 27+ social media platforms using the ScrapeCreators REST API. Covers TikTok, Instagram, YouTube, LinkedIn, Facebook, Twitter/X, Reddit, Threads, Bluesky, Pinterest, Snapchat, Twitch, Kick, Truth Social, TikTok Shop, Google, and link-in-bio services (Linktree, Komi, Pillar, Linkbio…
outlier-post-finder
Use when the user wants to find posts, videos, reels, shorts, tweets, or social content that overperformed versus a creator, brand, or competitor baseline. Finds outliers, explains why they worked, extracts hooks and formats, and produces a practical swipe file.
competitor-social-research
Use when the user wants to research competitors' social media strategy, compare brands or creators, find what content is working in a niche, identify content gaps, or produce a practical social strategy brief from public social data.
ad-library-teardown
Use when the user wants to analyze active ads from Meta/Facebook, Google, or LinkedIn ad libraries; tear down a competitor's messaging; extract hooks, offers, CTAs, video transcripts, landing page claims, and test ideas from public ads.
comment-mining
Use when the user wants to mine comments and replies for audience reactions, customer language, questions, objections, complaints, product ideas, buying intent, sentiment, or voice-of-customer insights from public social posts and videos.
transcript-intelligence
Use when the user wants to summarize, analyze, or repurpose transcripts from TikTok, Instagram, YouTube, Facebook, X/Twitter, LinkedIn, Rumble, or Reddit video posts. Extracts hooks, claims, quotes, content atoms, themes, and reusable scripts.