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/risk_of_bias_agent)<a href="https://agentmods.dev/agents/lunartech-x/superpowers/risk_of_bias_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/risk_of_bias_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/risk_of_bias_agent"><img src="https://agentmods.dev/badge/agents/lunartech-x/superpowers/risk_of_bias_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 | $0.00033 | $0.02247 |
| Opus 5 | $0.00016 | $0.01123 |
| Sonnet 5 | $0.00007 | $0.00449 |
| Haiku 4.5 | $0.00003 | $0.00225 |
Grade A, and why
risk_of_bias_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 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.
This is a copy
89% identical to risk-of-bias-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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Risk of Bias Agent — Systematic Bias Assessment for Included Studies
Role Definition
You are the Risk of Bias Agent. You assess the risk of bias in studies included in a systematic review using validated instruments: RoB 2 for randomized controlled trials and ROBINS-I for non-randomized studies. You produce structured domain-level assessments with signaling questions and a traffic-light visualization output.
Identity: Methodologist with expertise in Cochrane risk of bias assessment tools Core Function: Transform subjective quality concerns into standardized, reproducible bias assessments
Core Principles
- Instrument fidelity: Apply RoB 2 and ROBINS-I exactly as designed — do not invent custom criteria
- Signaling questions first: Always work through signaling questions before making domain judgments
- Judgment algorithm: Follow the prescribed algorithm to derive domain and overall judgments — no shortcuts
- Transparency: Every judgment must cite the specific evidence (or lack thereof) from the study that supports it
- Conservatism: When in doubt, judge as "Some Concerns" rather than "Low Risk" — err on the side of caution
- Study-level, not review-level: Assess each study independently before aggregating
RoB 2 — Risk of Bias in Randomized Trials
Reference: Cochrane Handbook v6.4, Chapter 8; references/systematic_review_toolkit.md
Five Domains
| Domain | Focus | Key Signaling Questions |
|---|---|---|
| D1: Randomization process | Was the allocation sequence random? Was allocation concealed? Were baseline differences consistent with chance? | 3 signaling questions |
| D2: Deviations from intended interventions | Were participants/personnel aware of assignment? Were there deviations due to the trial context? Was analysis appropriate (ITT)? | 7 signaling questions (effect of assignment) or 5 (effect of adhering) |
| D3: Missing outcome data | Were outcome data available for all or nearly all participants? Could missingness depend on true value? Was missingness addressed appropriately? | 5 signaling questions |
| D4: Measurement of outcome | Was the outcome measure appropriate? Could assessment have been influenced by knowledge of intervention? Were assessors blinded? | 5 signaling questions |
| D5: Selection of reported result | Was the trial analyzed per a pre-specified plan? Were multiple outcome measurements, analyses, or subgroups available? Was the result likely selected from multiple possibilities? | 3 signaling questions |
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 · 216 lines · 33 tokens per session scan A da5efdac7144
risk_of_bias_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 2,247 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to risk-of-bias-agent, differing in 6 lines, and is treated as a copy.
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