Create per-subsection evidence packs (NO PROSE): claim candidates, concrete comparisons, evaluation protocol, limitations, plus citation-backed evidence snippets with provenance. **Trigger**: evidence draft, evidence pack, claim candidates, concrete comparisons, evidence snippets, provenance, 证据草稿, 证据包, 可引用事实. **Use when**: `outline/subsection_briefs.jsonl` exists and you want evidence-first section drafting where every paragraph can be backed by traceable citations/snippets. **Skip if**: `outline/evidence_drafts.jsonl` already exists and is refined (no placeholders; >=8 comparisons per subsection; `blocking_missing` empty). **Network**: none (richer evidence improves with abstracts/fulltext). **Guardrail**: NO PROSE; do not invent facts; only use citation keys that exist in `citations/ref.bib`.

Planner-pass coverage + redundancy report for an outline+mapping, producing `outline/coverage_report.md` and `outline/outline_state.jsonl`. **Trigger**: planner, dynamic outline, outline refinement, coverage report, 大纲迭代, 覆盖率报告. **Use when**: you have `outline/outline.yml` + `outline/mapping.tsv` and want a verifiable, NO-PROSE planner pass before writing. **Skip if**: you don't want any outline/mapping diagnostics (or you have a frozen/approved structure and will not change it). **Network**: none. **Guardrail**: NO PROSE; do not invent papers; only report coverage/reuse and propose structural actions as bullets.

Write the survey's front matter files (Abstract, Introduction, Related Work, Discussion, Conclusion) in paper voice, with high citation density and a single evidence-policy paragraph. **Trigger**: front matter writer, introduction writer, related work writer, abstract writer, discussion writer, conclusion writer, 引言, 相关工作, 摘要, 讨论, 结论. **Use when**: you are in C5 (prose allowed) and need the paper-like shell to stop the draft reading like stitched subsections. **Skip if**: `Approve C2` is missing in `DECISIONS.md`, or `citations/ref.bib` is missing. **Network**: none. **Guardrail**: no invented facts/citations; no pipeline jargon in final prose; no repeated evidence disclaimers; only use keys present in `citations/ref.bib`.

Create a novelty/prior-work matrix comparing the submission’s contributions against related work (overlaps vs deltas). **Trigger**: novelty matrix, prior-work matrix, overlap/delta, 相关工作对比, 新颖性矩阵. **Use when**: peer review 中评估 novelty/positioning，需要把贡献与相关工作逐项对齐并写出差异点证据。 **Skip if**: 缺少 claims（先跑 `claims-extractor`）或你不打算做新颖性定位分析。 **Network**: none (retrieval of additional related work is out-of-scope unless provided). **Guardrail**: 明确 overlap 与 delta；尽量给出可追溯证据来源（来自稿件/引用/作者陈述）。

Extract per-subsection “anchor facts” (NO PROSE) from evidence packs so the writer is forced to include concrete numbers/benchmarks/limitations instead of generic summaries. **Trigger**: anchor sheet, anchor facts, numeric anchors, evidence hooks, 写作锚点, 数字锚点, 证据钩子. **Use when**: `outline/evidence_drafts.jsonl` exists and you want stronger, evidence-anchored writing in `sections/*.md`. **Skip if**: evidence packs are incomplete (fix `evidence-draft` first). **Network**: none. **Guardrail**: NO PROSE; do not invent facts; only select from existing evidence snippets/highlights.

Write a 1-page literature snapshot (`output/SNAPSHOT.md`) from a small core set + a bullets-only outline. **Trigger**: snapshot, literature snapshot, 速览, 48h snapshot, one-page snapshot, SNAPSHOT.md. **Use when**: 你要在 24-48h 内交付一个“可读的研究速览”（bullet-first，含关键引用），而不是完整 survey。 **Skip if**: 你已经进入 evidence-first survey 写作（有 `outline/evidence_drafts.jsonl` / `citations/ref.bib` / `output/DRAFT.md`），应改用 `subsection-writer`/`prose-writer`。 **Network**: none. **Guardrail**: 不发明论文/引用；引用只来自 `papers/core_set.csv`（或同 workspace 的候选池）；不写长段落（避免“像综述生成器”）。

Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`. **Trigger**: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF. **Use when**: `queries.md` 设置 `evidence_mode: fulltext`（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。 **Skip if**: `evidence_mode: abstract`（默认）；或你不希望进行下载/抽取（成本/权限/时间）。 **Network**: fulltext 下载通常需要网络（除非你手工提供 PDF 缓存在 `papers/pdfs/`）。 **Guardrail**: 缓存下载到 `papers/pdfs/`；默认不覆盖已有抽取文本（除非显式要求重抽）。

Deterministically merge per-section files under `sections/` into `output/DRAFT.md`, preserving outline order and weaving transitions from `outline/transitions.md`. **Trigger**: merge sections, merge draft, combine section files, sections/ -> output/DRAFT.md, 合并小节, 拼接草稿. **Use when**: you have per-unit prose files under `sections/` and want a single `output/DRAFT.md` for polishing/review/LaTeX. **Skip if**: section files are missing or still contain scaffolding markers (fix `subsection-writer` first). **Network**: none. **Guardrail**: deterministic merge only (no new facts/citations); preserve section order from `outline/outline.yml`.

Fill `outline/tables_index.md` from `outline/table_schema.md` + evidence packs (NO PROSE in cells; citation-backed rows). **Trigger**: table filler, fill tables, evidence-first tables, index tables, 表格填充, 索引表. **Use when**: table schema exists and evidence packs are ready; you want a compact, citation-backed index table to support later writing and Appendix table curation. **Skip if**: `outline/tables_index.md` already exists and is refined (>=2 tables; citations in rows; no placeholders). **Network**: none. **Guardrail**: do not invent facts; every row must include citations; do not write paragraph cells.

Polish a single H3 unit file under `sections/` into survey-grade prose (de-template + contrast/eval/limitation), without changing citation keys. **Trigger**: subsection polisher, per-subsection polish, polish section file, 小节润色, 去模板, 结构化段落. **Use when**: `sections/S*.md` exists but reads rigid/template-y; you want to fix quality locally before `section-merger`. **Skip if**: subsection files are missing, evidence packs are incomplete, or `Approve C2` is not recorded. **Network**: none. **Guardrail**: do not invent facts/citations; do not add/remove citation keys; keep citations within the same H3; keep citations subsection-scoped.

Write structured notes for each paper in the core set into `papers/paper_notes.jsonl` (summary/method/results/limitations). **Trigger**: paper notes, structured notes, reading notes, 论文笔记, paper_notes.jsonl. **Use when**: survey 的 evidence 阶段（C3），已有 `papers/core_set.csv`（以及可选 fulltext），需要为后续 claims/citations/writing 准备可引用证据。 **Skip if**: 还没有 core set（先跑 `dedupe-rank`），或你只做极轻量 snapshot 不需要细粒度证据。 **Network**: none. **Guardrail**: 具体可核对（method/metrics/limitations），避免大量重复模板；保持结构化字段而非长 prose。

Convert a taxonomy (`outline/taxonomy.yml`) into a bullet-only outline (`outline/outline.yml`) with sections/subsections. **Trigger**: outline builder, bullet outline, outline.yml, 大纲生成, bullets-only. **Use when**: structure 阶段（NO PROSE），已有 taxonomy，需要生成可映射/可写作的章节与小节骨架（每小节≥3 bullets）。 **Skip if**: 已经有批准过且可映射的 outline（避免无意义 churn）。 **Network**: none. **Guardrail**: bullets-only；移除 TODO/模板语句；每小节至少 3 个可检查 bullets。

Write one survey-quality paragraph from evidence packs (tension → contrast → evaluation anchor → limitation). **Trigger**: grad paragraph, paragraph micro-structure, argument paragraph, 研究生段落, 论证段落, 对比段, 段落写作. **Use when**: you are drafting `sections/S*.md` (H3 body) and want subsection-specific, evidence-bounded prose instead of templates. **Skip if**: evidence packs are missing/incomplete (fix `subsection-briefs`/`evidence-draft`/`evidence-binder` first), or `Approve C2` is not recorded in `DECISIONS.md`. **Network**: none. **Guardrail**: do not invent facts or citations; no placeholders/ellipsis; keep claims conservative when evidence is abstract-level (avoid repeating evidence-mode boilerplate in every paragraph).

Plan tutorial modules from a concept graph, including module objectives and sequencing, saving as `outline/module_plan.yml`. **Trigger**: module plan, tutorial modules, course outline, 模块规划, module_plan.yml. **Use when**: tutorial pipeline 的结构阶段（C2），已有 `outline/concept_graph.yml`，需要把概念依赖转成可教学的模块序列。 **Skip if**: 还没有 concept graph（先跑 `concept-graph`）。 **Network**: none. **Guardrail**: 每模块明确 objectives + outputs（最好含 running example 步骤）；避免 prose 段落。

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.

Prepares the codebase for a commit by formatting code and helping identify temporary comments.

Runs unit tests to quickly verify changes during the development loop.

Runs all necessary checks (lint, tests) and pushes to GitHub. Use this as the final safety gate.

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.