Install
openclaw skills install agent-survey-corpusDownload a small corpus of open-access arXiv survey/review PDFs about LLM agents and extract text for style learning. **Trigger**: agent survey corpus, ref corpus, download surveys, 学习综述写法, 下载 survey. **Use when**: you want to study how real agent surveys structure sections (6–8 H2), size subsections, and write evidence-backed comparisons. **Skip if**: you cannot download PDFs (no network) or you don't want local PDF files. **Network**: required. **Guardrail**: only download arXiv PDFs; store under `ref/` and keep large files out of git.
openclaw skills install agent-survey-corpusGoal: create a small, local reference library so you can learn from real agent surveys when refining:
This is intentionally not part of the pipeline; it is an optional, repo-level toolkit.
ref/agent-surveys/arxiv_ids.txtref/agent-surveys/pdfs/ref/agent-surveys/text/ref/agent-surveys/STYLE_REPORT.md (tracked; auto-generated summary)ref/agent-surveys/arxiv_ids.txt (one arXiv id per line).ref/agent-surveys/text/:
python scripts/run.py --helppython scripts/run.py --workspace . --max-pages 20--workspace <dir> (use . to write into repo root)--inputs <semicolon-separated> (default: ref/agent-surveys/arxiv_ids.txt)--max-pages <N> (default: 20)--sleep <seconds> (default: 1.0)--overwrite (re-download + re-extract)ref/:
python scripts/run.py --workspace . --max-pages 20python scripts/run.py --workspace /tmp/surveys --max-pages 30--sleep, or try fewer ids.--max-pages..gitignore (ref/**/pdfs/, ref/**/text/).