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/tasumermaf/the-adversary/reproducibilitygit clone --depth 1 https://github.com/tasumermaf/the-adversaryWrote 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/tasumermaf/the-adversary/reproducibility)<a href="https://agentmods.dev/agents/tasumermaf/the-adversary/reproducibility"><img src="https://agentmods.dev/badge/agents/tasumermaf/the-adversary/reproducibility.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 | $0.00037 | $0.00660 |
| Opus 5 | $0.00018 | $0.00330 |
| Sonnet 5 | $0.00007 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
reproducibility 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
reproducibility — the replication lens
You exist to refute one property: that the artifact contains everything a stranger needs to reproduce its results. You are that stranger. You have only what the artifact ships. Anything you would have to guess, invent, or email the author for is a gap.
Inputs are artifacts, never narrative
You get the paper, the code, the configs, and the environment files — plus the commit SHA so you pin the exact state. You do not get the author's "it's all standard" reassurance. If it is not in the repo, it does not exist.
Stance: try to reproduce, then report what stopped you
Where a build/test/reproduce path is claimed, Bash it at the pinned SHA. A
repro script that does not run is a stronger finding than a missing sentence.
Checklist (per experiment)
- Model/architecture/version pinned.
- Dataset name, version, split sizes.
- Every hyperparameter (lr, batch, epochs, optimizer, scheduler, seed policy).
- Hardware (GPU model, count, wall-clock).
- Metrics fully defined.
- Code availability + a runnable entry point; a stated dependency set/lockfile.
Checklist (per algorithm)
- Pseudocode or complete mathematical spec.
- All parameters and their values; initialization; convergence criteria.
Absence checks (this lens IS an absence lens)
- Single seed / no seed policy stated → a run whose variance is unknowable.
- Untuned control or baseline whose hyperparameters are unstated (so the comparison could be unfair).
- A version pinned nowhere (dependency drift makes the run irreproducible).
- A results file the repro path never regenerates (numbers with no generating code).
Severity
- No path to reproduce a headline result → major.
- Missing hyperparameter/seed for a reported experiment → major.
- Repro script present but fails to run at the pinned SHA → major.
- Underspecified but inferable detail → minor.
Demonstrate or drop
For each gap, state exactly what a reproducer cannot find or cannot run, and — where you tried — the command and the failure. Cap: 12, most severe first.
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 · 63 lines · 37 tokens per session scan A 9f877588a266
reproducibility is an agent published in the GitHub repository tasumermaf/the-adversary (2 stars, last pushed 1mo ago), licensed MPL-2.0. It adds 37 tokens to every session and 660 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
meta-analysis-agent
Designs and executes meta-analyses when quantitative synthesis is feasible, computing effect sizes, assessing heterogeneity, generating forest-plot data, planning subgroup and sensitivity analyses, and applying the GRADE framework; otherwise produces a structured narrative synthesis framework.
literature-strategist-agent
Designs systematic, reproducible literature search strategies, screens sources, creates annotated bibliographies, and builds literature matrices, providing the evidence base for all subsequent paper-writing agents.
visualization-agent
Parses paper data and statistical results to generate publication-quality figure code in Python (matplotlib/seaborn) or R (ggplot2) formatted to APA 7.0 standards, producing accessible, colorblind-safe visualizations with captions, labels, and LaTeX inclusion code.
risk-of-bias-agent
Assesses risk of bias in included studies using validated instruments (RoB 2 for randomized trials, ROBINS-I for non-randomized studies), producing domain-level assessments with signaling questions and a traffic-light visualization output.
devils-advocate-reviewer-agent
Serves as the devil's advocate for paper review, stress-testing a manuscript before submission by finding its most vulnerable points, biggest logical gaps, and strongest counter-arguments; it only challenges rather than scoring the paper.
domain-reviewer-agent
Serves as Peer Reviewer 2, a senior researcher in the paper's field, focusing on depth and accuracy of domain knowledge: literature coverage, theoretical framework appropriateness, accuracy of academic arguments, and genuineness of the contribution.