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 asoiso/trend-radar --skill trend-monitorgit clone --depth 1 https://github.com/asoiso/trend-radarWrote 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/asoiso/trend-radar/trend-monitor)<a href="https://agentmods.dev/skills/asoiso/trend-radar/trend-monitor"><img src="https://agentmods.dev/badge/skills/asoiso/trend-radar/trend-monitor/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/asoiso/trend-radar/trend-monitor"><img src="https://agentmods.dev/badge/skills/asoiso/trend-radar/trend-monitor.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 | $0.00171 | $0.01686 |
| Opus 5.5 | $0.00068 | $0.00674 |
| Sonnet 5.5 | $0.00034 | $0.00337 |
| Haiku 4.5 | $0.00017 | $0.00169 |
Grade A, and why
trend-monitor 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 2mo 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
trend-monitor
Multi-platform hotspot sweep. Pulls the latest crawl batch from the trendradar MCP, runs preset-focus and emergent keyword extraction in parallel, then summarizes either by platform or by topic.
When to activate
Trigger on phrases like:
- 今日热点 / 今天有什么热点 / 今天热搜
- 各平台热搜 / 看一下当前热搜 / 全网热点 / 全网在聊什么
- 现在在火什么 / 最近在聊什么
- what's trending / what's trending now / multi-platform sweep / hotspot snapshot
- 给我看一下 zhihu / weibo / douyin / bilibili / toutiao / baidu / thepaper / ifeng / tieba / cls-hot / wallstreetcn-hot 的热搜
If the user names a single article, a specific topic deep-dive, or a date in the past, this is not the right skill — defer to a search/topic-analysis skill instead. This skill is for the live cross-platform snapshot.
Workflow
-
Resolve the time window. If the user mentions any date that isn't "now / 现在 / 今天 / today", call
mcp__trendradar__resolve_date_rangefirst and reuse that range in the next steps. For "今天 / now / latest", skip this step. -
Pull the latest news batch. Call
mcp__trendradar__get_latest_newswithlimit=80andinclude_url=False. Setinclude_url=Trueonly if the user explicitly asked for links. If the user named specific platforms, passplatforms=[...](e.g.["weibo", "zhihu"]); otherwise let the tool return the full set. -
Run keyword extraction in parallel. Issue both calls in the same response:
mcp__trendradar__get_trending_topicswithextract_mode="keywords",top_n=20— preset focus list (curated themes the project tracks).mcp__trendradar__get_trending_topicswithextract_mode="auto_extract",top_n=20— emergent terms surfaced from this batch only.
-
Cross-reference. Build two buckets before drafting the output:
- 全网共振: keywords that appear on ≥3 platforms in this batch (pulled from either extraction mode).
- 新冒头: terms present in
auto_extractresults but absent from thekeywordspreset list — these are the emergent signals worth flagging.
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.
- 2mo ago First seen · 121 lines · 171 tokens per session scan A 31fccf7e22e4
trend-monitor is a skill published in the GitHub repository asoiso/trend-radar (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 171 tokens to every session and 1,686 once invoked, about $0.0007 per session on Opus 5.5. 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.
trend-monitor compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| trend-monitorasoiso/trend-radar | Skill | 2 | 6mo ago | Apache-2.0 | A | 171 | Claude Code, Codex |
| ops-demoSnowflake-Labs/cocoplus | Skill | 727 | yesterday | MIT | A | 46 | Claude Code |
| ai-brainOotto-AI/claude-content-skills | Skill | 408 | yesterday | MIT | A | 95 | Claude Code |
| comment-responderOotto-AI/claude-content-skills | Skill | 408 | yesterday | MIT | A | 94 | Claude Code |
| hashtag-keyword-researchOotto-AI/claude-content-skills | Skill | 408 | yesterday | MIT | A | 56 | Claude Code |
| trend-spotterOotto-AI/claude-content-skills | Skill | 408 | yesterday | MIT | A | 74 | Claude Code |
| diarySnowflake-Labs/cocoplus | Skill | 727 | yesterday | MIT | A | 12 | Claude Code, Codex |