Loading...
Loading...
Found 108 Skills
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Maintain a paper-facing evidence board that aligns claims, experiments, figures, tables, sections, reviewer risks, and next actions during ML/AI paper writing. Use this skill whenever writing exposes missing experiments, new results require paper changes, reviewer simulation reveals evidence gaps, claims need support checks, figures/tables need mapping to claims, or the user wants a live paper evidence board before submission.
Review ML or AI experiment figures, tables, plots, captions, result narratives, and paper visual style before they are shown in a paper, advisor meeting, report, slide deck, rebuttal, or submission. Use this skill whenever the user has experimental results, plots, tables, metrics, screenshots, captions, draft result sections, or wants to audit figure style choices such as color, typography, markers, symbols, line widths, sizing, and venue-consistent visual conventions.
Build a retrospective or forward-looking work timeline from git commits, project docs, user notes, or chat records, then output a Markdown and/or HTML report with a Gantt chart or timeline visualization. Use when the user wants to review past work across one or more projects, explain time allocation to a mentor, summarize what was done in a period, or plan the next phase with a timeline.
Diagnose surprising, negative, unstable, or ambiguous ML/AI experiment results and decide whether to debug implementation, rerun experiments, change metrics or baselines, revise the algorithm, narrow the paper claim, park, or kill a direction. Use this skill whenever results do not match expectations, a method fails, metrics conflict, seeds vary, baselines beat the method, plots look suspicious, or the user asks what to do next after experimental results.
Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.
Performs ARA Seal Level 2 semantic epistemic review on Agent-Native Research Artifacts, scoring six dimensions (evidence relevance, falsifiability, scope calibration, argument coherence, exploration integrity, methodological rigor) and producing a constructive, severity-ranked report with a Strong Accept-to-Reject recommendation. Use after Level 1 structural validation passes, when an ARA needs an objective epistemic critique before publication or release.
Plan, draft, and revise ML/AI limitations, scope, failure cases, ethics, broader impact, and conclusion caveats so they control claim boundaries without undermining the paper. Use when the user wants limitation wording, scope statements, failure-case interpretation, ethics/broader-impact text, or overclaim reduction.
Read research outline, launch independent agent for each item for deep research. Disable task output.
Add items (research objects) to existing research outline.
Summarize deep research results into markdown report, cover all fields, skip uncertain values.
Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.