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scout
Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use for natural-science or engineering tasks, scientific software routing, simulation, dataset analysis, model fitting, package checks, HPC-through-shell work, validation, and evidence-backed scientific claims using DeepScientist's `artifact.science(...)` Science Evidence Graph. Includes a progressive-disclosure catalog of FermiLink skilled-scipkg package cards.
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.
Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics.
Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a research paper, not only medical papers. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and performs lightweight verification with cross-platform Python tooling by default.
Polish, restructure, or translate academic prose into Nature-leaning English using the paper-architecture and writing-strategy principles from Scientific English Writing & Communication, with phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English.
Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.
| name | scout |
| description | Use when a quest needs problem framing, literature scouting, dataset or metric clarification, or baseline discovery before deeper work. |
| skill_role | stage |
Use this skill when the quest does not yet have a stable research frame. The goal is to make the task frame concrete enough that a heavier stage can start with confidence.
Use scout when:
Do not use scout when:
Resolve only the minimum framing unknowns that change the next anchor, then stop once baseline or idea becomes durable and obvious.
baseline, idea, or both.scout become endless exploration.memory.list_recent(...) and memory.search(...).artifact.arxiv(...) for actual paper reads.artifact.arxiv(paper_id=..., full_text=False) instead of defaulting to a raw PDF.artifact.arxiv(...) only for actual paper reading, and set full_text=True only when needed.scout should normally hand off to baseline or idea as soon as the next move is decision-ready.Before scout can end, all applicable checks should be true:
Follow the shared interaction contract injected by the system prompt. Only send a richer scout milestone when the framing ambiguity actually shrank or the next anchor became clear. For ordinary active work, prefer a concise progress update once work has crossed roughly 6 tool calls with a human-meaningful delta, and do not drift beyond roughly 12 tool calls or about 8 minutes without a user-visible update.
shell_command / command_execution in this skill.bash_exec(...).artifact.git(...) before raw shell git commands.artifact.read_quest_documents(...), artifact.get_quest_state(...), and memory.* instead of shelling out.Prefer the following sources in order:
Do not let the scout stage rest on vague recollection alone.
The scout stage should usually establish four layers:
Before spending time scouting, first verify whether the current quest already contains enough framing in:
brief.mdplan.mdstatus.mdSUMMARY.mdIf the answer is already clear, exit quickly and move to the correct next anchor.
The main skill keeps the control surface in front. For the longer search and handoff notes, read:
references/paper-triage-playbook.mdreferences/literature-scout-template.mdreferences/eval-contract-template.mdreferences/baseline-shortlist-template.mdreferences/operational-guidance.mdUse them when:
Record a blocked state if scouting cannot proceed because:
A blocked scout result should state:
Do not hide a blocked scout stage behind generic literature chatter.
Exit the scout stage once all of the following are true:
If the stage relied on external search, the literature scouting report must also be durable before exit.
Typical next anchors:
baselineideascout only if the remaining blocker is explicit and durableA good scout pass makes the next anchor obvious or makes the blocker explicit enough that the system stops guessing.