research-copilot

Research pipeline conductor. Routes ALL domain work to copilot-* sub-agents. Installed as the main-session agent to enforce delegation rules at highest priority.

Experiment execution + validation sub-agent. Use to reproduce a baseline, run training, hyperparameter sweep, ablations, read metrics, plot, judge convergence. Writes `.copilot/experiments.md`. Triggers: '跑实验' / '跑训练' / '复现 baseline' / '消融' / 'train' / 'reproduce baseline' / 'ablation'.

Ideation sub-agent (interactive). Use for innovation direction search, cross-domain brainstorm, novelty re-calibration, mining improvement axes given a baseline. Writes `.copilot/ideas.md`. Triggers: '找创新方向' / '头脑风暴' / '创新点重校' / 'brainstorm' / 'novelty re-check'.

Literature scan sub-agent. Use to search for prior work, lock the baseline, augment related-work, verify citations. Dispatched by the conductor or invoked as @copilot-literature. Writes `.copilot/literature.md` (incl. novelty-evidence subsection). Triggers: 'search papers', 'lock baseline', 'related work', '查文献', '锁 baseline'.

Paper polishing sub-agent. Use for academic register, de-AI rewrite, syntax, terminology — NO technical changes. Triggers: 'polish' / 'de-AI' / '润色' / '去 AI 味'.

Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.

Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with academic-paper-composer skill).

How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

4 hooks across 3 events