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 agentmods add skills/epistates/sparx/researchnpx skills add Epistates/sparX --skill researchgit clone --depth 1 https://github.com/Epistates/sparXWrote 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/epistates/sparx/research)<a href="https://agentmods.dev/skills/epistates/sparx/research"><img src="https://agentmods.dev/badge/skills/epistates/sparx/research.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.01078 |
| Opus 5 | $0.00020 | $0.00539 |
| Sonnet 5 | $0.00008 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
research 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 4d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Topic & Trend Research for X Content
Research what's being discussed, debated, and shared in a specific niche to identify high-potential content opportunities.
Input
The user provides:
- A niche or topic to research (e.g., "Rust", "local LLMs", "developer tools")
- Or asks "what should I post about?"
- Or wants to understand what's trending
Process
Step 1 — Web Research
Search for current conversations and trends using WebSearch:
- Search X/Twitter directly for the niche topic + "2026" to find recent discussions
- Search Reddit (r/programming, niche subreddits) for hot topics
- Search Hacker News for trending technical discussions
- Search for recent releases/announcements in the niche
- Search for controversies or debates (these drive highest engagement)
Use WebSearch with queries like:
site:x.com [niche topic] 2026[niche] trending discussion March 2026[niche] controversial opinion 2026[niche] new release announcement 2026
Deep-read promising results using WebFetch to extract full content from the most relevant search results. This gives you the actual conversations, not just titles.
If chrome is available and you need to browse X's live feed for real-time conversations, you can navigate to X search:
https://x.com/search?q=[topic]&f=live— latest posts on the topic- Read the actual discussions, not just search snippets
Step 2 — Identify Content Opportunities
Categorize findings into opportunity types:
Hot Takes — Controversial discussions where you can add a unique perspective
- Look for: debates, strong opinions, "unpopular opinion" threads
- Signal: High reply counts indicate engagement potential
Tutorials — Questions people are asking that you can answer
- Look for: "How do I...", Stack Overflow trending, common mistakes
- Signal: Repeat questions indicate unmet demand
Announcements — New releases, updates, or changes you can contextualize
- Look for: GitHub releases, product launches, API changes
- Signal: Timing matters — first-mover advantage in commentary
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.
- 4d ago First seen · 109 lines · 41 tokens per session scan A 28d2e6ae803a
research is a skill published in the GitHub repository Epistates/sparX (3 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 1,078 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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