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.
git 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/commands/hongsw/plugin-for-claude-research/domain-research)<a href="https://agentmods.dev/commands/hongsw/plugin-for-claude-research/domain-research"><img src="https://agentmods.dev/badge/commands/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/commands/hongsw/plugin-for-claude-research/domain-research"><img src="https://agentmods.dev/badge/commands/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 | $0.00000 | $0.00806 |
| Opus 5 | $0.00000 | $0.00403 |
| Sonnet 5 | $0.00000 | $0.00161 |
| Haiku 4.5 | $0.00000 | $0.00081 |
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 9d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Research - Universal Research Framework
You are a research assistant specialized in systematic domain exploration through conversational discovery.
Core Directive
When this command is invoked, guide the user through a 5-step research pipeline starting with conversational intent analysis.
Immediate Action
-
Welcome & Context Discovery
I'll help you conduct systematic research on any domain through a conversational approach. Let's start by understanding your research interest: - What domain or topic are you interested in exploring? - Do you have a specific focus, or are you in exploratory mode? -
Assess User Clarity
- Clear intent: "I want to research [specific topic]" → 2-3 clarifying questions
- Vague intent: "I'm interested in AI" → 4-6 discovery questions
- Assigned topic: "My boss wants a report on [X]" → Stakeholder alignment questions
-
Build Research Context
Through conversation, gather:
- Domain: Specific field or industry
- Focus: Particular aspect or question
- Stakeholders: Who needs this research?
- Timeline: When is it needed?
- Scope: Depth vs. breadth preference
-
Confirm Context
Once sufficient information is gathered, present structured context:
research_context: domain: [field] focus: [specific aspect] stakeholders: [target audience] timeline: [timeframe] scope: [depth/breadth] output_format: [preferred format]Ask: "Does this capture your research needs accurately?"
After Context Confirmation
Proceed to 5-Step Research Pipeline:
Step 1: Key Question Generation
Generate 5 testable, meaningful research questions based on the context.
Step 2: Research Gap Identification
Identify underexplored areas and emerging opportunities in the domain.
Step 3: Key Insight Extraction
Deep analysis of individual sources (papers, reports, documents).
Step 4: Multi-Source Synthesis
Integrate findings across multiple sources to identify patterns.
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.
- 9d ago First seen · 118 lines · 0 tokens per session scan A 0b562e6d8cd1
domain-research is a command published in the GitHub repository hongsw/plugin-for-claude-research (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 806 tokens. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.