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/lllllllama/RigorPilot-SkillsWrote 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/commands/lllllllama/rigorpilot-skills/ai-research-reproduction)<a href="https://agentmods.dev/commands/lllllllama/rigorpilot-skills/ai-research-reproduction"><img src="https://agentmods.dev/badge/commands/lllllllama/rigorpilot-skills/ai-research-reproduction/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/commands/lllllllama/rigorpilot-skills/ai-research-reproduction"><img src="https://agentmods.dev/badge/commands/lllllllama/rigorpilot-skills/ai-research-reproduction.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 | $0.00014 | $0.00119 |
| Opus 5 | $0.00007 | $0.00060 |
| Sonnet 5 | $0.00003 | $0.00024 |
| Haiku 4.5 | $0.00001 | $0.00012 |
Grade A, and why
ai-research-reproduction 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 12d 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.
What it actually says
Read and follow @skills/ai-research-reproduction/SKILL.md.
Apply that skill to the current repository unless $ARGUMENTS clearly points to a different local repository path or adds extra constraints.
Keep Rigor Reproduce trusted and README-first:
- prefer documented inference or evaluation before training
- keep changes conservative and auditable
- write the standard
repro_outputs/bundle
Additional user context: $ARGUMENTS
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.
- 12d ago First seen · 17 lines · 14 tokens per session scan A a7439999acf8
ai-research-reproduction is a command published in the GitHub repository lllllllama/RigorPilot-Skills (487 stars, last pushed 5d ago), licensed MIT. It adds 14 tokens to every session and 119 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.
Other commands, from other repositories
cite-check
Verify that citations actually exist and that the claims they support are faithful to the cited source. Runs deterministic existence checks (Crossref / OpenAlex / Semantic Scholar / arXiv) plus a claim-faithfulness pass via the alterlab-citation-verifier skill.
lit-review
Run a systematic, reproducible literature review on a topic and return an APA 7.0 annotated bibliography with a documented search strategy. Invokes the alterlab-deep-research pipeline in lit-review mode.
ml-project
Start a professional AI/ML research-engineer workflow for a task (any domain - CV, medical imaging, NLP/LLM, tabular, time-series). Researches papers first, picks the best method, trains/evaluates rigorously and honestly.
ars-abstract
ARS academic-paper abstract-only mode — bilingual abstract + keywords.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.