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 n24q02m/claude-plugins --skill research-topicgit clone --depth 1 https://github.com/n24q02m/claude-pluginsWrote 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/n24q02m/claude-plugins/research-topic)<a href="https://agentmods.dev/skills/n24q02m/claude-plugins/research-topic"><img src="https://agentmods.dev/badge/skills/n24q02m/claude-plugins/research-topic/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/n24q02m/claude-plugins/research-topic"><img src="https://agentmods.dev/badge/skills/n24q02m/claude-plugins/research-topic.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 | $0.00068 | $0.00806 |
| Opus 5.5 | $0.00027 | $0.00322 |
| Sonnet 5.5 | $0.00014 | $0.00161 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
research-topic 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 16d 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.
This is a copy
100% identical to research-topic — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-topic
Drive wet's extract(action="agent") to answer a research question
end to end: one search round + concurrent extracts of the top hits + a
single LLM synthesis pass that preserves numbered [N] citations
matching the returned sources.
Use this skill when:
- The user asks an open-ended question that needs multiple sources.
- "Summarise the current state of X."
- "What's the latest on Y?"
- "Compare approaches to Z."
- The user needs a quoted, cited answer (the citations are first-class output, not an afterthought).
Do NOT use this skill when:
- The user already gave you a specific URL -- call
extract(action="extract"). - The user wants a single search result list -- call
search(action="web"). - The question is about library API documentation -- call
search(action="docs_query")against a Tier 1 / locked stack.
Steps
-
Restate the question to the user in 1-2 sentences (calibration: confirm scope before spending tokens).
-
Pick
max_urlsbased on breadth:- 3-5 for a tight question (single technology, single timeframe).
- 6-10 for a broad survey (multiple competitors, multi-year window).
- Hard ceiling is 20 (cost guard).
-
Pick
synthesis_modelonly if the user asked for a specific model. Otherwise omit and let wet auto-detect fromLLM_MODELS/GEMINI_API_KEY/OPENAI_API_KEY/XAI_API_KEY. -
Call
extract(action="agent", query="<question>", max_urls=<N>)Optional knobs:
synthesis_model="...",token_budget=<int>(default 10000; raise for long-form questions, lower for tight cost control). -
Quote the synthesised Markdown verbatim to the user, then list the sources from the
sourcesarray as clickable URLs. Ifper_url_metadatashows anyerror, mention which URL failed and that the synthesis used the remaining N-K sources. -
If wet returns
Error: no LLM provider detected, surface the exact error to the user (do not silently retry againstsearch(action="research")); they need to set one of the supported API keys before agent works.
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.
- 16d ago Changed 955e32f6a63a
- 1mo ago First seen · 83 lines · 68 tokens per session scan A 253c4689d3bd
research-topic is a skill published in the GitHub repository n24q02m/claude-plugins (4 stars, last pushed yesterday), licensed Apache-2.0. It adds 68 tokens to every session and 806 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to research-topic, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
project
A single starting point for setting up an AI-assisted project in Claude Cowork. It asks questions about the work, reviews available add-ons, and creates project instructions, custom agents, and connected workflows.
doc-html-slide
A renderer that turns presentation content into a single HTML slide deck that opens directly in a browser. It creates a 16:9 slide sequence with navigation, fullscreen viewing, printing to PDF, and speaker-note controls.
cs-channel-message
A channel-message writing tool for search ads, advertising, customer relationship messages, and app notifications. It uses the NCM sequence—Need, Channel, Moment, Message, and CTA—to adapt wording to where and when customers see it.
media-higgsfield-explainer
A Higgsfield workflow for making non-photorealistic narrated explainer videos. It pairs each narration line with a 10-second animated clip and joins the clips into one finished video.
media-gpt-image-prompt
An image-prompt writing tool that turns a natural-language idea into detailed prompts for GPT Image 2.5. It can also adapt the same idea for Gemini 3 Pro Image and Midjourney.
career-portfolio
A portfolio-building skill for Korean job seekers that organizes projects and explains their work for development, design, marketing, and planning roles.