ai-research-reproduction (lllllllama)
- Research
Faithfully re-runs a published deep-learning repository to verify its documented results — reading the README first, picking the smallest honest inference or evaluation target, executing it under strict no-silent-edits patch rules, and logging evidence, deviations, and human sign-off points into a standardized outputs bundle. It is about REPRODUCING someone else's already-published numbers, not summarizing a paper, gathering fresh sources, or drawing business conclusions.
A situation it fits
You downloaded the freely available implementation of a famous image-classifier and need to independently check whether its headline accuracy claim was reached by running the lightest possible benchmark, documenting every deviation from the original setup, and signing off with your own numbers in a neatly packaged file before your committee reviews it.
2 scenarios in the bank answer to ai-research-reproduction (lllllllama). The rest are in the game.
Skills it gets confused with
These share a family with ai-research-reproduction (lllllllama), which is another way of saying they are the ones you might reach for by mistake.
- market-research (affaan-m)Reads the competitive and market landscape to inform a business decision — TAM/SAM/SOM sizing, competitor and product comparisons, investor and fund diligence, and technology scans — with every claim sourced, contrarian evidence included, and the output ending in a recommendation rather than a summary. It ANALYZES the market you are entering or funding, not reproducing research results, synthesizing user interviews, or organizing your own research files.
- research-papers (firecrawl)Runs a literature review: discovers and synthesizes published scholarly work on a topic — journal papers, preprints, whitepapers, and technical reports — through semantic search over a paper index (PubMed, bioRxiv, medRxiv, arXiv), expanding from seed papers into the surrounding citation family and verifying claims inside individual PDFs. It is about SURVEYING what the published literature already says on a subject, not re-running one paper's code to check its numbers, compressing your own interviews and tickets into themes, or sizing a commercial market.
- synthesize-research (anthropics)Turns a pile of already-collected qualitative input — interview notes, survey responses, support tickets, sales-call feedback — into one structured read: themes grouped and counted, findings ranked by frequency and impact, and recommendations tied back to the evidence. It is about COMPRESSING many scattered sources into a single coherent synthesis, not running an experiment, sizing a market, or filing and circulating the raw materials.
Knowing what ai-research-reproduction (lllllllama) does is the easy half. Telling it apart from the others under time pressure is the game.
Today's sessionai-research-reproduction (lllllllama) is part of lllllllama/rigorpilot-skills. Licence: MIT. The description above was written for this game, not taken from the skill.