技能创建器
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创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
ieee-summarize【数据与研究】Summarize, organize, and normalize messy research materials into an IEEE-ready writing brief before manuscript drafting. Use when Codex needs to process a folder or collection of experiment logs, code files, AI chat exports, idea notes, literature summaries, related-work notes, copied introduction excerpts, Zotero-derived paper summaries, tables, fig…
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this skill before ieee-writing when the user's research materials are scattered, chronological, duplicated, or not yet in manuscript structure. The output is not final prose; it is an evidence-grounded research brief that later writing skills can use.
Do not turn messy notes into polished claims by guessing. Preserve uncertainty, source paths, missing evidence, and contradictions.
manifest.yaml.always_load.material_scope: folder / pasted-notes / code-and-logs / literature-pack / mixed.output_target: research-brief / writing-input / experiment-summary / related-work-seed / claim-evidence-seed.cleanup_level: inventory / standard / deep.scripts/inventory_research_folder.py <folder>.rg --files <folder>.solid, partial, unclear, or missing for each evidence block.ieee-writing, ieee-experiment, ieee-citation, ieee-reviewer, or ieee-polishing.Default output:
Detected axes: material_scope=..., output_target=..., cleanup_level=...
Material inventory
- Notes:
- Code:
- Experiments:
- Literature:
- Figures/tables:
- Unknown / skipped:
IEEE Research Brief
- Working title:
- Research object:
- Operating condition / constraints:
- Engineering harm:
- Core method:
- Implementation clues:
- Experiment evidence:
- Related-work seed:
- Contribution candidates:
- Conclusion candidates:
- Missing evidence:
- Contradictions / risks:
Next skill handoff
- Recommended next skill:
- Prompt-ready brief:
For folder-level work, create or update a Markdown file only when the user asks for a file. Suggested filename: ieee-research-brief.md.
Do not invent paper titles, citations, datasets, numerical results, code behavior, reviewer comments, or experiment outcomes.
Do not treat AI chat exports as verified evidence. Label them as ideation unless confirmed by code, logs, data, or papers.
Do not summarize copied paper introductions into plagiarism-ready text. Extract reusable background logic, domain motivation, and citation roles instead.
Do not remove negative results or failed experiments; classify them as limitation, ablation clue, or future-work evidence.