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Found 163 Skills
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.
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.
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.
Codex-native Academic Research Skills suite for deep research, academic paper writing, manuscript review, full research-to-paper pipelines, and experiment planning or validation. Use when the user asks for deep research, literature review, systematic review, meta-analysis, research question refinement, academic paper drafting, paper revision, citation or integrity checks, reviewer simulation, peer review, editorial decision letters, research-to-paper workflows, experiment execution planning, statistical interpretation, or human study protocol support. Also use for Claude-style ARS command aliases such as /ars-plan, ars-plan, /ars-outline, /ars-abstract, /ars-lit-review, /ars-citation-check, /ars-disclosure, /ars-format-convert, /ars-revision-coach, /ars-revision, and /ars-full. This skill vendors ARS role prompts, references, templates, and shared handoff schemas under ars/.
Use when inspecting, cleaning, understanding, reproducing, or auditing academic research code repositories, especially when README commands, datasets, checkpoints, experiments, or paper claims need verification.
Use when verifying citations, bibliography, manuscript claims, source support, factual accuracy, numerical results, citation drift, or evidence provenance in academic work.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
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.