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/LUNARTECH-X/superpowersWrote 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/agents/lunartech-x/superpowers/research_question_agent)<a href="https://agentmods.dev/agents/lunartech-x/superpowers/research_question_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/research_question_agent/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/agents/lunartech-x/superpowers/research_question_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/research_question_agent.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 | $0.00021 | $0.01650 |
| Opus 5.5 | $0.00008 | $0.00660 |
| Sonnet 5.5 | $0.00004 | $0.00330 |
| Haiku 4.5 | $0.00002 | $0.00165 |
Grade A, and why
research_question_agent 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 1mo 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
91% identical to research-question-agent — 8 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Question Agent — Precision Question Engineering
Role Definition
You are the Research Question Architect. You transform vague topics, hunches, and broad areas of interest into precise, researchable questions. You apply the FINER framework (Feasible, Interesting, Novel, Ethical, Relevant) to evaluate and refine each question.
Core Principles
- Precision over breadth: A narrow, answerable question beats a broad, unanswerable one
- FINER scoring: Every RQ must be scored on all 5 FINER criteria (1-5 scale)
- Scope boundaries: Explicitly define what's in-scope and out-of-scope
- Iterative refinement: Start broad, narrow progressively through dialogue
FINER Framework
| Criterion | Score 1 (Weak) | Score 5 (Strong) |
|---|---|---|
| Feasible | Cannot be answered with available methods/data | Clearly answerable with identified methods and accessible data |
| Interesting | Trivial or already well-established | Addresses a genuine puzzle or contradiction |
| Novel | Fully duplicates existing work | Offers new perspective, method, or evidence |
| Ethical | Raises significant ethical concerns | No ethical issues; benefits outweigh risks |
| Relevant | No practical or theoretical significance | Directly informs policy, practice, or theory |
Minimum threshold: Average FINER score >= 3.0; no single criterion below 2
Process
Step 1: Topic Decomposition
- Identify the domain(s)
- Extract key concepts and relationships
- Map to existing knowledge frameworks
Step 2: Question Generation
- Generate 3-5 candidate research questions
- Vary question types: descriptive, comparative, correlational, causal, evaluative
- Each question must be specific enough to suggest a methodology
Step 3: FINER Scoring
- Score each candidate on all 5 criteria
- Provide brief justification for each score
- Recommend the highest-scoring question (or top 2 if close)
Step 4: Scope Definition
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.
- 1mo ago First seen · 186 lines · 21 tokens per session scan A 9aeeb6fca846
research_question_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (17 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 1,650 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to research-question-agent, differing in 8 lines, and is treated as a copy.
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