# Research Survey Loop > Long-running survey workflow for robotics, embodied AI, computer vision, world models, navigation, manipulation, 3D scene understanding, and adjacent research. Use when Codex needs to create or continue a multi-round literature review task, maintain stable task documents (`task.md`, `round_log.md`, `current_task.md`, `survey.md`), search Nature/Science plus top CV and robotics venues before arXiv, migrate relevant local PDFs from `papers/` into task-local `sources/`, read PDFs in chunks of at most 10 pages, and incrementally write a Chinese Markdown survey with relative local citations or web links. - Skill: `immortalqx/research-survey-loop` (Agent Skill, multi-file: 11 files) - Install (CLI): `npx skillmds@latest add immortalqx/research-survey-loop` - Raw SKILL.md: https://api.skillmd.com/api/skills/immortalqx/research-survey-loop/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: Immortalqx (https://skillmd.com/u/immortalqx) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/immortalqx/research-survey-loop --- # Research Survey Loop Survey topic: `$ARGUMENTS` ## Overview Use this skill for long-running survey work with a primary focus on robotics, embodied AI, computer vision, world models, spatial intelligence, navigation, manipulation, and 3D perception. The workflow can extend to adjacent computer science topics, but the bundled search priorities, venue choices, and writing templates are optimized for robotics and embodied AI communities rather than for all research domains equally. The default working style is: 1. Create or resume one task directory under `survey_tasks//` 2. Keep `task.md` stable unless the user explicitly changes the task itself 3. Append `round_log.md` every round 4. Rewrite `current_task.md` every round so the next unattended run has a precise entry point 5. Grow one long `survey.md` by category paragraphs rather than per-paper cards 6. Move task-absorbed local PDFs out of the root `papers/` pool into the task's own `sources/papers/` This skill is academic-first. Only include `industry_report/` when the user explicitly wants industry or market material mixed into the survey. ## Directory Contract Every survey task lives under: ```text survey_tasks// task.md round_log.md current_task.md survey.md sources/ papers/ supplementary/ ``` Rules: - Treat the root `papers/` directory as an unsorted staging pool, not as the long-term citation location. - Once a local PDF is truly used by a task, move it into that task's `sources/papers/`. - If another task later needs the same paper, copy it from the first task's `sources/papers/` into the new task instead of pointing back to the old path. - Use relative local links from `survey.md`, such as `./sources/papers/example.pdf`. - If a source cannot be downloaded locally, cite the canonical web URL instead. ## Required Resources Read these bundled files before acting: - `references/source-priority.md` for the fixed search order and venue priorities - `references/writing-rules.md` for reading depth, citation format, migration rules, and round-closing discipline Use these bundled templates and scripts instead of improvising new file layouts: - `assets/task-template.md` - `assets/round-log-template.md` - `assets/current-task-template.md` - `assets/survey-template.md` - `scripts/init_task.py` - `scripts/fetch_sources.py` - `scripts/extract_pdf_chunk.py` ## Workflow ### 1. Bootstrap or Resume If the task directory does not exist, run: ```bash python3 scripts/init_task.py "TOPIC" --workspace-root "$PWD" ``` If the task already exists, read in this order before doing any new work: 1. `task.md` 2. `current_task.md` 3. The latest round in `round_log.md` 4. `survey.md` Do not rewrite `task.md` unless the user explicitly changes the task definition. ### 2. Search Web First Default search order is fixed: 1. Nature / Science and related journals 2. Top CV venues 3. Top robotics venues 4. arXiv 5. Local `papers/` pool as supplement, de-dup, or migration source Use the current agent's web search / fetch capabilities for publisher-first searching. Use `scripts/fetch_sources.py` to normalize downloads and imports into the task directory. Typical commands: ```bash python3 scripts/fetch_sources.py search "TOPIC" --task-dir "survey_tasks/TOPIC-SLUG" --max-per-source 5 python3 scripts/fetch_sources.py download --task-dir "survey_tasks/TOPIC-SLUG" --arxiv-id 2402.07556 ``` Search rules: - Prefer official publisher or venue pages over tertiary summaries. - Prefer formal published versions over arXiv when both exist. - Keep arXiv as a rapid frontier source and fallback download source. - The bundled `fetch_sources.py search` command uses public Semantic Scholar and arXiv APIs for normalized results. - If one source is unavailable, continue with the others rather than blocking. ### 3. Import and Migrate Local Papers Only migrate local PDFs that the current task actually absorbs. To move a root-level paper into the task: ```bash python3 scripts/fetch_sources.py import-local --task-dir "survey_tasks/TOPIC-SLUG" "papers/file.pdf" ``` To reuse a paper from another survey task: ```bash python3 scripts/fetch_sources.py reuse-task-paper --task-dir "survey_tasks/TOPIC-SLUG" --from-task "survey_tasks/OTHER-TASK" "sources/papers/file.pdf" ``` Migration rules: - Move from root `papers/` into the task when the paper becomes part of the task's reading queue or survey evidence. - Record the original and destination paths in `round_log.md`. - Remove migrated items from the `current_task.md` pending migration list. - Never keep citing the old root `papers/` path in `survey.md`. ### 4. Read PDFs in Chunks Never read more than 10 PDF pages at once. Use: ```bash python3 scripts/extract_pdf_chunk.py "survey_tasks/TOPIC-SLUG/sources/papers/file.pdf" --start-page 1 --end-page 10 --json ``` Rules: - Long papers must be read in consecutive windows such as `1-10`, `11-20`, `21-30`. - Do not write firm conclusions into `survey.md` before understanding: - the research problem - the core method - the claimed contribution - the evidence supporting the claim - the main limitation - Use survey papers to build taxonomy and terminology first. - Use original papers to strengthen category paragraphs and comparisons. ### 5. Write the Survey Incrementally `survey.md` is one long Markdown document, not a pile of independent paper cards. Writing rules: - First round: build rough categories and coarse survey paragraphs. - Later rounds: deepen one category at a time by integrating newly understood papers into the relevant paragraph. - If a paper is not yet fully understood, keep it in `current_task.md` or note it in `round_log.md` as pending. - Preserve terminology consistency across rounds. - Keep every claim traceable to a relative local path or canonical web link. ### 6. Close the Round At the end of every round: 1. Append a new round block to `round_log.md` 2. Rewrite `current_task.md` 3. Leave the next round with: - explicit search queue - explicit reading queue - explicit pending migrations - explicit next paragraph targets in `survey.md` Do not finish a round with only a vague note like "continue later." ## Output Shape The four main files have fixed roles: - `task.md`: stable task contract - `round_log.md`: append-only history - `current_task.md`: editable current execution plan - `survey.md`: long-form Chinese survey with category paragraphs Default language is Chinese, while preserving English titles, venues, and technical terms where useful. ## Failure Handling - If a source cannot be downloaded, keep the best canonical URL and continue. - If Semantic Scholar or arXiv access is temporarily unavailable, keep working with the remaining sources. - If a PDF is longer than expected, continue chunked reading rather than skipping it. - If a paper is relevant but still unclear, mark it as pending instead of writing a fake summary. - If a task directory already exists, preserve `task.md` by default and only recreate missing files.