research-co-pilot

Draft long-form manuscript sections from methodology + analysis outputs

Stress-test a research protocol the way an ethics committee (IRB / REC / HREC) would

Draft grant-proposal sections (NSF, NIH, ERC, Wellcome, Horizon Europe, foundations)

Clean, analyze, model, and visualize quantitative data — Python or R, with reproducible scripts

Brainstorm and pressure-test research ideas, questions, and angles

Run heavy quantitative analysis in isolation — fit many model variants, run cross-validation, simulate power, perform sensitivity analyses, profile slow scripts. Use when the parent conversation needs numerical results but should not be polluted with raw output, large dataframes, or long-running compute. Returns a tight summary: results table, key plots, model comparison, code path, and interpretation hooks.

Draft long-form manuscript prose in isolation — full sections (intro / methods / results / discussion / abstract) or full first drafts. Use when the parent conversation needs a polished draft but should not be flooded with thousands of words of prose. Returns a structured digest plus the draft as files.

Find, retrieve, and extract structured information from academic sources at scale. Use when you need to read many papers/sources to support a literature review, fact-check claims with primary citations, or build a structured corpus from a topic or starter list. Returns one structured extract per source — citation, claim, evidence type, sample, methods, key findings, limitations, and quality flags — without polluting the parent context with raw paper content.

Independent second-look on a colleague's quantitative analysis — script + data + report. Rebuilds the analysis in fresh context (no contamination from the original narrative) and reports whether the conclusions hold under: re-execution, alternative specifications, sensitivity to outliers and missing-data handling, multiple-comparisons correction, and pre-specified vs exploratory clarity. Returns a tight validation memo with confidence judgment, not a full re-analysis.

Process qualitative transcripts and text data at scale — clean and standardize transcripts, apply a given codebook to a corpus, suggest emergent codes from open-ended responses, and prepare hand-coding deliverables. Use when working with many transcripts/responses where reading every line in the parent context would be impractical. Returns a structured coded output plus a summary, NOT raw transcript dumps.

Format citations and bibliographies in any major academic style — APA 7, MLA 9, Chicago (notes-bibliography and author-date), Harvard, Vancouver, IEEE, AMA, and discipline-specific journal styles. Converts between formats, builds reference lists from raw input, validates DOIs, and generates BibTeX/RIS exports. Trigger when: user asks to "format this citation", "convert to APA", "bibliography in Chicago", "reference list", "BibTeX", "fix my references", "is this APA correct", or runs /cite.

End-to-end quantitative data work — cleaning, exploration, statistical testing, modeling, visualization, and reproducible scripting in Python or R. Handles messy CSVs, survey data, time series, panel data, and small-to-medium datasets. Produces analysis scripts and a written interpretation. Trigger when: user mentions "clean this data", "analyze", "statistics", "regression", "EDA", "explore the data", "fit a model", "visualize", "summary stats", "hypothesis test", "ANOVA", "t-test", "correlation", "Python script", "R script", "pandas", "tidyverse", or runs /analyze.

Act as a research ethics committee — stress-test a protocol the way an IRB / REC / HREC would. Reviews informed consent, risk-benefit balance, vulnerable populations, data privacy, deception, debriefing, payment, dual-use risks, AI/LLM use in research, and equity in recruitment. Produces a committee-style decision letter with required revisions, recommended changes, and approval pathway. Useful before IRB submission, when responding to IRB feedback, when drafting ethics sections of a paper or grant, or when a protocol changes mid-study. Trigger when: user mentions "ethics review", "IRB", "REC", "HREC", "ethics committee", "ethics statement", "informed consent", "is this study ethical", "vulnerable population", "deception in research", "Belmont", "Helsinki", "GDPR research", "ethics approval", "ethics application", or runs /ethics.

Draft sections of a grant proposal — specific aims / lay summary / significance / innovation / approach / broader impacts / budget justification / data management plan / biosketch — adapted to the funder's format and review criteria. Supports NSF, NIH (R01, R21, R03, F31, F32, K-series), ERC (Starting, Consolidator, Advanced), Wellcome, Horizon Europe, NSF GRFP, foundation grants, and other major schemes. Works from research-brainstorm and methodology-advisor outputs, plus the funder's published criteria. Trigger when: user mentions "grant proposal", "specific aims", "NSF", "NIH", "ERC", "Wellcome", "Horizon Europe", "research proposal", "fellowship application", "biosketch", "broader impacts", "data management plan", "lay summary", "case for support", or runs /grant.

Conduct rigorous, fact-checked academic literature reviews. Synthesizes sources, traces citation chains, flags weak claims, and produces structured outputs (narrative, systematic, scoping, or thematic). Trigger when: user asks for a "literature review", "lit review", "background research", "state of the field", "what does the research say about…", "summarize the literature on…", "find sources on…", or runs /lit-review. Works from sources the user provides (PDFs, links, citations) AND from web/database search when allowed.