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GitHub 저장소

codex-pro-bridge

codex-pro-bridge에는 WILLOSCAR에서 수집한 skills 7개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.

수집된 skills
7
Stars
2
업데이트
2026-07-10
Forks
0
직업 범위
직업 카테고리 2개 · 100% 분류됨
저장소 탐색

이 저장소의 skills

bundle-algorithm-context
소프트웨어 개발자

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 that needs local evidence.

2026-07-10
gpt-pro-algorithm-pipeline
소프트웨어 개발자

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 connected through one reproducible task thread.

2026-07-10
gpt-pro-question-window
소프트웨어 개발자

Bridge Codex to a signed-in ChatGPT/GPT Pro conversation, including scoped file upload, conversation reuse, raw answer capture, and later Codex verification. Use for normal GPT Pro questions and as the browser/persistence foundation for other Codex Pro Bridge skills; do not use for local tasks that need no external reasoning.

2026-07-10
experiment-plan-generator
소프트웨어 개발자

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.

2026-07-10
gpt-pro-paper-brainstormer
기타 중등 후 교사

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.

2026-07-10
gpt-pro-research-algorithm-reviewer
소프트웨어 개발자

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 analysis.

2026-07-10
implementation-consistency-checker
소프트웨어 개발자

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.

2026-07-10