Menu
SkillsMP has collected 10 skills from WILLOSCAR/codex-pro-bridge. Open a skill to review its source and details.
Showing 10 of 10 collected skills.
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,…
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…
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…
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…
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…
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…
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.
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…
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.
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.