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-deep-divegit 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-deep-dive)<a href="https://agentmods.dev/skills/asoiso/trend-radar/trend-deep-dive"><img src="https://agentmods.dev/badge/skills/asoiso/trend-radar/trend-deep-dive/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-deep-dive"><img src="https://agentmods.dev/badge/skills/asoiso/trend-radar/trend-deep-dive.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 | $0.00103 | $0.02378 |
| Opus 5 | $0.00051 | $0.01189 |
| Sonnet 5 | $0.00021 | $0.00476 |
| Haiku 4.5 | $0.00010 | $0.00238 |
Grade A, and why
trend-deep-dive 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.
How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
trend-deep-dive
Single-topic, multi-angle investigation skill. Given one topic (a person, event, product, hashtag, or phrase), produce a structured report covering heat curve, lifecycle stage, viral anomaly assessment, near-future prediction, related satellite events, and sentiment evolution. Backed by the trendradar MCP.
When to activate
Activate when the user's intent is to understand one topic in depth (not to scan many topics, not to compose a campaign). Trigger phrases include:
- "深挖 XX" / "把 XX 说清楚" / "XX 是怎么演变的"
- "XX 的趋势" / "XX 的热度曲线" / "XX 这几天怎么样"
- "XX 处于什么阶段" / "XX 的生命周期" / "XX 还能火多久"
- "XX 会不会火" / "XX 接下来怎么走" / "predict X 的走势"
- "XX 是不是异常热点" / "有没有人为推动" / "是不是被刷的"
- "XX 的舆情" / "舆情演化" / "网友怎么看 XX"
- "deep dive on X" / "go deep on X"
If the user asks about many topics at once, redirect to trend-monitor. If they want to make a piece of content off the topic, redirect to viral-forge. If they want a report deliverable, hand off to trend-report after finishing the analysis.
Decision tree for analysis_type
mcp__trendradar__analyze_topic_trend accepts an analysis_type argument. Pick exactly one based on the user's primary goal:
| User goal (paraphrased) | analysis_type |
Notable args |
|---|---|---|
| "热度曲线 / 看变化 / 这几天怎么样 / 趋势是啥" | trend |
granularity="day" (default) or "hour" if window < 3 d |
| "现在处于哪个阶段 / 还能火多久 / 是萌芽还是衰退" | lifecycle |
none beyond topic/date_range |
| "是不是异常热点 / 有没有人为推动 / 是不是被刷的" | viral |
spike_threshold (e.g. 3.0), time_window (e.g. "24h") |
| "未来几小时/几天会怎样 / 还会不会涨 / predict 走势" | predict |
lookahead_hours (default 24), confidence_threshold (0.6) |
Rules of thumb:
- If the user asks two of the above in one breath, run them sequentially in this order:
trend→lifecycle→viral→predict. Do not parallelize differentanalysis_typecalls on the same topic — each call already returns curve data the next one can reference. - If unsure, start with
trend(cheapest, gives a baseline curve), then escalate.
What ships with it
1 file 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 · 167 lines · 103 tokens per session scan A 17ff0cff50db
trend-deep-dive is a skill published in the GitHub repository asoiso/trend-radar (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 103 tokens to every session and 2,378 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 2026-08-31.
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