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research-topic

research-topic is a skill for Claude Code from n24q02m/wet-mcp. It costs 68 tokens per session (808 once invoked), scanned A, original, Apache-2.0.

A workflow for researching an open-ended question by searching the web, reading several relevant results, and producing a summary with citations. It is intended for questions that need multiple sources or current information.

In plain words
What is it for?
Researching a topic, summarizing its current state, comparing approaches, and answering questions that require several web sources.
Why use it?
It reduces the work of finding, comparing, and citing sources. The result keeps references connected to the claims they support.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the wet-mcp plugin — 5 skills, 1 MCP server shipped together

not rated 17repo today A scan SkillSpector: pass 68 tokens original Apache-2.0

Good fit Researching a topic, summarizing its current state, comparing approaches, and answering questions that require several web sources.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/n24q02m/wet-mcp/research-topic
View source ↗ n24q02m/wet-mcp mcp.n24q02m.com
Install

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.

Any agent
npx skills add n24q02m/wet-mcp --skill research-topic
Clone the repo
git clone --depth 1 https://github.com/n24q02m/wet-mcp

Made for: Claude Code.

Or install wet-mcp, the plugin that ships this one along with the rest of its 5 skills, 1 MCP server.

Wrote 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.

agentmods badge for research-topic

README.md
[![agentmods](https://agentmods.dev/badge/skills/n24q02m/wet-mcp/research-topic/github.svg)](https://agentmods.dev/skills/n24q02m/wet-mcp/research-topic)
Your own site
<a href="https://agentmods.dev/skills/n24q02m/wet-mcp/research-topic"><img src="https://agentmods.dev/badge/skills/n24q02m/wet-mcp/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.

agentmods 80×15 button for research-topic

Your own site · 80×15
<a href="https://agentmods.dev/skills/n24q02m/wet-mcp/research-topic"><img src="https://agentmods.dev/badge/skills/n24q02m/wet-mcp/research-topic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 808 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00068 $0.00808
Opus 5 $0.00034 $0.00404
Sonnet 5 $0.00014 $0.00162
Haiku 4.5 $0.00007 $0.00081

Measured 10d ago against content hash 253c4689d3bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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 10d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/run.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

skills/research-topic/SKILL.md · 83 lines

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-mcp'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

  1. Restate the question to the user in 1-2 sentences (calibration: confirm scope before spending tokens).

  2. Pick max_urls based 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).

  3. Pick synthesis_model only if the user asked for a specific model. Otherwise omit and let wet auto-detect from LLM_MODELS / GEMINI_API_KEY / OPENAI_API_KEY / XAI_API_KEY.

  4. 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).

  5. Quote the synthesised Markdown verbatim to the user, then list the sources from the sources array as clickable URLs. If per_url_metadata shows any error, mention which URL failed and that the synthesis used the remaining N-K sources.

  6. If wet returns Error: no LLM provider detected, surface the exact error to the user (do not silently retry against search(action="research")); they need to set one of the supported API keys before agent works.

Read the full file on GitHub · 83 lines

Files

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.

Changes

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.

  1. 10d ago First seen · 83 lines · 68 tokens per session scan A 253c4689d3bd

Subscribe to this mod's changes

research-topic is a skill published in the GitHub repository n24q02m/wet-mcp (17 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 808 once invoked, about $0.0003 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 2026-08-30.

Data and AIPythonResearchcrawl4aimcpmcp-servermodel-context-protocolopen-source
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