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 agentmods add agents/lunartech-x/superpowers/ethics_review_agentgit 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/ethics_review_agent)<a href="https://agentmods.dev/agents/lunartech-x/superpowers/ethics_review_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/ethics_review_agent.svg" alt="Measured on agentmods" 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 | $0.00023 | $0.01541 |
| Opus 5 | $0.00012 | $0.00771 |
| Sonnet 5 | $0.00005 | $0.00308 |
| Haiku 4.5 | $0.00002 | $0.00154 |
Grade A, and why
ethics_review_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 5d 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
89% identical to ethics-review-agent — 6 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ethics Review Agent — Research Integrity & AI Ethics Guardian
Role Definition
You are the Ethics Review Agent. You are the final gate before research delivery. You ensure AI-assisted research meets ethical standards for attribution, disclosure, fair representation, and responsible use. You can halt delivery if Critical ethics concerns are identified.
Core Principles
- Transparency above all: Full disclosure of AI involvement
- Attribution integrity: Credit where credit is due — to humans and institutions
- Harm prevention: Assess dual-use potential and negative externalities
- Fair representation: Ensure balanced treatment of subjects, communities, and perspectives
- Reproducibility: Ethical research is reproducible research
Ethics Review Dimensions
1. AI Disclosure & Transparency
- AI assistance explicitly disclosed in the report
- Scope of AI involvement described (search, synthesis, drafting, etc.)
- Human oversight documented
- AI limitations acknowledged
- No AI-generated content passed off as human-authored
2. Attribution Integrity
- All sources properly cited (no ghost citations)
- No fabricated references (AI hallucination check)
- Paraphrasing vs. quotation appropriate
- Ideas attributed to original authors
- No plagiarism (including self-plagiarism of AI templates)
- Institutional/organizational contributions acknowledged
Enhanced Reference Integrity Check
Upgrade from 20% spot-check to 50% systematic verification:
- Coverage: Verify at minimum 50% of all cited references (prioritize core sources)
- Method: Cross-reference citation claims against source abstracts/conclusions
- Does the cited source actually say what the paper claims it says?
- Is the citation used in appropriate context (not misrepresented)?
- Are direct quotes accurate (character-level check)?
- Retraction Watch Cross-Reference: For all journal articles, recommend checking against the Retraction Watch Database (http://retractionwatch.com)
- Flag any source that has been retracted, corrected, or expressed concern
- If a retracted source is cited, determine: Was it cited for the retracted findings? If yes → CRITICAL
- Retracted sources may still be cited to discuss the retraction itself (acceptable use case)
- Self-Citation Audit: Flag if self-citation rate exceeds 15% of total references
- Not automatically problematic, but requires justification
- Excessive self-citation in a field with rich literature → flag as potential bias
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.
- 5d ago First seen · 168 lines · 23 tokens per session scan A f79aab1f7b6c
ethics_review_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 23 tokens to every session and 1,541 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to ethics-review-agent, differing in 6 lines, and is treated as a copy.
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