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 hongsw/plugin-for-claude-research --skill domain-researchgit clone --depth 1 https://github.com/hongsw/plugin-for-claude-researchWrote 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/hongsw/plugin-for-claude-research/domain-research)<a href="https://agentmods.dev/skills/hongsw/plugin-for-claude-research/domain-research"><img src="https://agentmods.dev/badge/skills/hongsw/plugin-for-claude-research/domain-research/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/hongsw/plugin-for-claude-research/domain-research"><img src="https://agentmods.dev/badge/skills/hongsw/plugin-for-claude-research/domain-research.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 | $0.00044 | $0.01397 |
| Opus 5 | $0.00022 | $0.00698 |
| Sonnet 5 | $0.00009 | $0.00279 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
domain-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 10d 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal Research Framework
Core Purpose
A domain-agnostic research framework that guides users from broad exploration to specific domain research through conversational intent analysis. Works for any field:
- Manufacturing AI → Healthcare AI → FinTech → EdTech → Sustainability → and beyond
What It Does
- Conversational Discovery: Guide users through natural dialogue to define their research context
- Structured Context Building: Transform vague interests into actionable research parameters
- Systematic Research Pipeline: 5-step process from questions to action plans
- Evidence-Based Insights: Generate findings grounded in research and data
- Practical Application: Convert insights into executable roadmaps
Target Audience
This framework serves domain practitioners across any field:
- Industry Professionals: Engineers, managers, analysts seeking evidence-based guidance
- Academic Researchers: Faculty, students bridging theory and practice
- Business Leaders: Decision-makers needing structured research for strategy
- Consultants: Professionals providing research-backed recommendations
- Policy Makers: Those needing comprehensive domain understanding
Research Pipeline
Step 0: Conversational Intent Analysis
Prompt: prompts/intent-analyzer.md
Purpose: Guide users from vague interests to structured research context through dialogue
Process:
- Open invitation → Context deepening → Synthesis → Confirmation
- Adaptive questioning based on user type (clear/vague/assigned/exploratory) Output: Structured YAML research context
Step 1: Key Question Generation
Prompt: prompts/key-questions.md
Purpose: Generate 5 testable, meaningful research questions
Input: Research context from Step 0
Output: Prioritized questions with importance, impact, and methodology
Step 2: Research Gap Identification
Prompt: prompts/research-gaps.md
Purpose: Identify underexplored areas and limitations in existing research
Input: Key questions from Step 1
Output: 4 priority gaps with proposed research ideas
What ships with it
8 files 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.
- 10d ago First seen · 192 lines · 44 tokens per session scan A f84990a12022
domain-research is a skill published in the GitHub repository hongsw/plugin-for-claude-research (20 stars, last pushed 4mo ago), licensed MIT. It adds 44 tokens to every session and 1,397 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 2026-08-30.
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