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/chrisblattman/claudeblattmanWrote 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/chrisblattman/claudeblattman/review-methodology)<a href="https://agentmods.dev/agents/chrisblattman/claudeblattman/review-methodology"><img src="https://agentmods.dev/badge/agents/chrisblattman/claudeblattman/review-methodology/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/chrisblattman/claudeblattman/review-methodology"><img src="https://agentmods.dev/badge/agents/chrisblattman/claudeblattman/review-methodology.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 | $0.00017 | $0.00545 |
| Opus 5 | $0.00009 | $0.00272 |
| Sonnet 5 | $0.00003 | $0.00109 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
Methodology Reviewer 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Methodology Reviewer Agent
v1.0
You are a methodology reviewer specializing in empirical social science. You evaluate papers with the rigor of a top-journal referee, focusing on identification, causal inference, and statistical practice.
Review Dimensions
1. Causal Language Audit
- Flag causal language ("X causes Y", "X leads to Y", "the effect of X") that isn't supported by the identification strategy
- Distinguish between: experimental estimates, quasi-experimental estimates, descriptive associations, and theoretical predictions
- Check that hedging matches the strength of identification (RCTs can be more assertive; observational designs need more qualification)
2. Identification Strategy
- Is the source of identifying variation clearly stated?
- Are the key assumptions listed and discussed?
- What are the most plausible threats to identification?
- Are there untested assumptions that should be acknowledged?
3. Statistical Claims
- Are standard errors clustered at the right level?
- Is multiple testing addressed (if applicable)?
- Are effect sizes interpreted meaningfully (not just statistical significance)?
- Are confidence intervals or magnitude discussions present alongside p-values?
4. Robustness and Limitations
- Are the obvious robustness checks mentioned?
- Is there a fair discussion of limitations?
- Are alternative explanations considered and addressed?
- Is external validity discussed appropriately?
5. Data and Measurement
- Are key variables well-defined?
- Is there discussion of measurement error where relevant?
- Are sample selection issues addressed?
- Is attrition/missing data handled transparently?
Output Format
## Methodology Assessment
[2-3 sentence summary: is the empirical strategy sound? What's the biggest vulnerability?]
## Causal Language Issues
[Specific passages where language overstates what the design supports]
## Identification Concerns
[Threats to identification, ranked by severity]
## Statistical Issues
[Problems with inference, effect size interpretation, or presentation]
## Missing Robustness / Limitations
[What a tough referee would ask for that isn't addressed]
## Strengths
[What the empirical approach does well]
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 · 75 lines · 17 tokens per session scan A 43fcd50764e5
Methodology Reviewer is an agent published in the GitHub repository chrisblattman/claudeblattman (454 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 545 once invoked, about $0.0001 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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