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SkillsMP は WILLOSCAR/codex-pro-bridge から 10 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 10 件中 10 件を表示しています。
Coordinate the single-user Auto Research system across Primary, Research Orchestrator, Track Mentor, Student, local Codex chats, and one-to-many GPT Pro web conversations. Use when an Auto Research request concerns Research Line identity, Idea Variants,…
原文の言語: 英語
Bind one local research repository to one existing or new ChatGPT Project, synchronize stable Project Sources, inspect Project tasks, or repair Project routing. Use when the user mentions a ChatGPT Project, shared files across conversations, several research…
原文の言語: 英語
Bridge Codex to the correct signed-in ChatGPT/GPT Pro conversation through a Chrome DevTools MCP-first browser route, automatically choosing standalone or a bound ChatGPT Project, with scoped upload, conversation reuse, raw answer capture, and later Codex…
原文の言語: 英語
Fast lane for high-frequency, fine-grained, parallel GPT Pro review. Send one mature idea, research proposal, or atomic sub-task from one research line to GPT Pro as a standalone, single-round Review Probe that never touches the Bridge Project source-sync…
原文の言語: 英語
Build a compact, immutable GPT Pro evidence bundle from repository code, configs, docs, logs, Codex notes, and recent bridge events. Use before algorithm review, paper brainstorm, experiment analysis, implementation consistency review, or any GPT Pro round…
原文の言語: 英語
Run the end-to-end Codex to GPT Pro to Codex loop for algorithm, research, experiment, or implementation-consistency work. Use when the user wants scoped evidence bundling, deep external review, local verification, experiments, and safe implementation…
原文の言語: 英語
Use GPT Pro for research-paper brainstorming, claim sharpening, novelty analysis, related-work positioning, reviewer objections, and experiment story design. Best for algorithm/research ideas where Codex alone is too implementation-biased.
原文の言語: 英語
Deep GPT Pro algorithm/research/pipeline review. Use for RL, reward modeling, OPD, agentic workflows, search/QA, training/eval pipelines, or algorithm proposals where Codex needs stronger hypothesis, experiment, ablation, data leakage, novelty, and go/no-go…
原文の言語: 英語
Convert an algorithm review or research idea into a prioritized experiment matrix with baselines, ablations, metrics, success/failure criteria, commands, logging requirements, and decision rules. Use after GPT Pro review or before implementing experiments.
原文の言語: 英語
Verify that an algorithm proposal, GPT Pro review, code implementation, configs, data splits, eval scripts, logs, and experiment commands are mutually consistent. Use before trusting results or after Codex implements an algorithm/pipeline change.
原文の言語: 英語