Haojae/scipilot-cite-skill

Letzte erfasste Quellaktivität: 15. Juni 2026 um 03:39

SkillsMP-Katalog aktualisiert: 2. November 2026 um 18:02

gesammelte Skills: 1

GitHub-Stars: 87
GitHub-Forks: 4

Mit einem KI-Assistenten installieren

Diesen Prompt kopieren und in den verwendeten KI-Assistenten einfügen.

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/Haojae/scipilot-cite-skill.

README des Repositorys

README-Quelldatei: README.md

README-Quelldatei lesen

Vollständige README anzeigen

# SciPilot-cite-skill > SciPilot Skills family. Citation copilot for academic writing. > SciPilot Skills 家族成员 — 学术写作的引用副驾驶。 [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python: 3.9+](https://img.shields.io/badge/Python-3.9%2B-3776AB.svg)](#dependencies--依赖) [![Status: v1.0.0](https://img.shields.io/badge/Status-v1.0.0-success.svg)](#) [![Hallucination Audited: Gate 8](https://img.shields.io/badge/Hallucination%20Audited-Gate%208-c41e3a.svg)](#gate-8-physical-hallucination-audit--幻觉门控) [![Claude Code Skill](https://img.shields.io/badge/Claude%20Code-Skill-orange.svg)](https://claude.com/claude-code) A [Claude Code](https://claude.com/claude-code) / [Codex](https://github.com/openai/codex) / Cursor Skill that **discovers, verifies, and inserts academic references** into your LaTeX or Word manuscript. Backed by three independent scholarly APIs with an authenticity-first verification pipeline that **never fabricates a citation**. > **Distinguishing feature — physical hallucination gate.** Every citation in the delivered bibliography must survive an end-to-end audit that re-verifies 100% of entries against live Crossref / Semantic Scholar / OpenAlex and reconciles them with a tamper-evident JSONL evidence log written during search and verification. No log entry, no live API hit, no citation — period. **"Never fabricates" is enforced by code, not by prompt.** See [Gate 8](#gate-8-physical-hallucination-audit--幻觉门控) below. > [中文文档](#中文文档) | [English](#english) --- ## 中文文档 ### 概览 `scipilot-cite-skill` 解决学术写作中最容易出错也最伤诚信的环节 —— **引用**。支持两种输入： - **模式 A — 论文模式**：读取你的 `.tex` 或 `.docx` 论文，在正文对应章节插入引用 + 生成 References - **模式 B — 片段模式**：你给一段话或一个观点，输出能支撑它的真实文献列表（不动文档）两种模式都通过 **Semantic Scholar + OpenAlex + Crossref** 三源并行检索近年高引文献，对每篇候选执行 **DOI 解析 + 多源交叉验证**，把无法验证的全部丢弃，按你指定的格式（IEEE / APA 7 / Nature / Vancouver / GB/T 7714）输出。 **Stage 0 主动询问 12 项参数**：输入模式、文献数量、引用格式、年限、目标章节、必引论文、期刊偏好、**期刊分区**（Q1 / 中科院 1-2 区）、**作者偏好**、**作者单位偏好**、是否含预印本、额外关键词。不会默默用默认值。 **核心承诺：** - 一篇都不编造 — 验证失败的文献直接丢弃，宁缺毋滥 - 引用编号严格按正文首次出现顺序，零跳跃零孤儿 - 原文一字不动 — 只追加引用标记和 References，不修改任何已有内容 - **物理幻觉门控** — 交付前 100% 重审计，任何无溯源凭证的引用都被自动拦截 ### 为什么这个 Skill 不一样：Gate 8 幻觉门控大多数 LLM 写引用的助手把"不编造"写在提示词里，然后祈祷模型遵守。这在长上下文、API 失败、用户施压等情境下经常崩溃——典型表现是**真期刊+假页码、真作者+假 DOI 的"高仿"引用**，连资深审稿人都很难一眼识破。 scipilot-cite-skill 把"不编造"从**口号**升级成**机器可证伪的契约**。具体做法： ``` search_papers.py → 写 evidence_log.jsonl （每篇 API 响应都落盘） ↓ verify_paper.py → 写 verification_log.jsonl（每条验证 verdict 落盘） ↓ LLM 准备 final_papers.json （Stage 4 用户确认后的最终清单） ↓ audit_no_hallucination.py 末端独立审计： · 100% 重新调 Crossref/S2/OpenAlex 实时复核 · 与 verification_log.jsonl 逐条对账 · 检测 verdict drift（日志说 VERIFIED 但实时说 UNVERIFIED） · 任何一条没溯源 / 日志缺失 / 漂移 → exit code 2 → 拒绝交付 ``` **三种典型幻觉，全部物理拦截：** | 幻觉模式 | 拦截机制 | |---|---| | LLM 凭印象添加论文 | 在 `verification_log.jsonl` 中**找不到** `paper_id` 条目 → FAIL | | API 一时返错信息被采信 | 末端**实时复核** Crossref，若现在查不到 → FAIL | | 真期刊 + 编造的 page/volume | DOI 解析返回真元数据，比对发现不一致 → MISMATCH → FAIL | 这就是 README 顶部那个红色 badge `Hallucination Audited: Gate 8` 的意义——它不只是装饰，对应的是工程上 `scripts/audit_no_hallucination.py` 里**真正会跑、真正会 exit 2、真正会阻止交付**的代码。 ### Stage 0 主动询问机制（12 项参数）很多 AI 引用工具默认填一组参数就开始干，结果出来的引用对不上用户预期。`scipilot-cite-skill` 把 Stage 0 设计成**强制对齐**阶段——不获得用户明确确认就不进入检索。具体询问： | 类别 | 询问项 | 备注 | |---|---|---| | 输入模式 | 模式 A（论文）/ 模式 B（片段） | 最先问 | | 基础参数 | 目标对象 / 文献数量 / 引用格式 | 必问 | | 检索范围 | 年份 / 是否含预印本 | 告知默认 | | 插入位置 | 目标章节（仅模式 A） | 默认自动判断 | | 质量偏好 | 必引论文 / 期刊会议 / **期刊分区** / **作者偏好** / **作者单位** / 额外关键词 | 用户可逐项跳过 | 收齐后向用户**口头复述全部参数**，等待"开始"确认。 ### 核心特性 | 维度 | 实现 | |---|---| | 数据源 | Semantic Scholar（200M+ 论文）、OpenAlex（250M+）、Crossref（150M+ DOI 权威） | | 检索方式 | 三源 ThreadPool 并行，按引用量降序合并，DOI 主键去重 + 标题模糊去重 | | 真实性验证 | **三档分级 + 末端再审计**：`VERIFIED` / `LIKELY_REAL` / `UNVERIFIED`，再过 Gate 8 100% 重审 | | **幻觉门控** | **Stage 7 第 0 项：`audit_no_hallucination.py` 阻塞性运行；FAIL 即拒绝交付** | | 证据账本 | `evidence_log.jsonl` + `verification_log.jsonl` 由脚本写，LLM 无写权限 | | 引用格式 | IEEE / APA 7 / Nature / Vancouver / GB/T 7714-2015 + BibTeX 导出 | | 文档格式 | LaTeX `.tex`（`\cite{}` + `thebibliography` 或 BibTeX）、Word `.docx`（保持原格式） | | 工作流 | 8 个 Stage 严格顺序，每个 Stage 内置质量门 | | 依赖 | `requests` + `python-docx` + `python-Levenshtein`，**全部 API 免费**，无需 key | ### 安装 #### 方式 A：让 Claude Code / Codex 自己装（推荐）在终端启动 Claude Code 或 Codex，直接说： ``` 请帮我安装这个 Skill：https://github.com/Haojae/scipilot-cite-skill.git ``` AI 会自动 `git clone` 到正确目录（`~/.claude/skills/` 或 `~/.codex/skills/`），并提示你装 Python 依赖。 #### 方式 B：手动 clone ```bash git clone https://github.com/Haojae/scipilot-cite-skill.git ~/.claude/skills/scipilot-cite-skill pip install -r ~/.claude/skills/scipilot-cite-skill/requirements.txt ``` Codex 用户把目标目录换成 `~/.codex/skills/`，Cursor 用户换成 `.cursor/skills/`。 #### 方式 C：下载 ZIP 1. 在 GitHub 仓库页面点 `Code` → `Download ZIP` 2. 解压到 `~/.claude/skills/scipilot-cite-skill/` 3. `pip install -r requirements.txt` ### 使用 #### 1. 自然语言触发启动 Claude Code 后随便用一句中文或英文： **模式 A — 论文插入**： ``` 帮我的 paper.tex 加 15 篇参考文献，用 IEEE 格式，只在 Introduction 和 Related Work 章节加。 ``` ``` 读取 thesis.tex，补充 10 篇 GB/T 7714 格式引用，只要中科院 1-2 区期刊，排除预印本。 ``` **模式 B — 片段支撑**（新）： ``` 我有这段话需要文献支撑： "近年来基于扩散模型的图像生成方法在医学影像合成上取得显著进展，能够缓解小样本数据稀缺问题。" 帮我找 8 篇 2022-2026 的近年高引文献，IEEE 格式，偏好 MIT/Stanford/清华的工作。 ``` ``` 这个观点有文献支撑吗： "LLM 通过 RLHF 训练能显著降低有害输出。" 找 5 篇 Nature/Science/NeurIPS 顶刊文献。 ``` Skill 会在 Stage 0 主动询问 12 项参数（见上节），全部确认后再开始检索 → 验证 → 输出。**关键决策点会等你拍板**。 #### 2. 命令行直接调脚本不通过 Skill 也能独立使用每个脚本： ```bash # 三源并行检索 python scripts/search_papers.py "diffusion model" --limit 10 --json > papers.json # 验证一个 DOI python scripts/verify_paper.py doi 10.1038/s41586-024-07421-0 \ --title "Detecting hallucinations..." --year 2024 # 单条引用格式化 echo '{"title":"...","authors":["..."],"year":2024,"venue":"...","doi":"..."}' | \ python scripts/format_citation.py --style ieee --number 1 # 端到端插入 LaTeX python scripts/insert_citations_latex.py paper.tex papers.json --style ieee # 端到端插入 Word python scripts/insert_citations_docx.py paper.docx papers.json --style nature ``` ### 工作流（8 个 Stage） ``` Stage 0 参数收集 (篇数 / 格式 / 年限 / 预印本 / 特殊要求) | Stage 1 论文分析 (章节抽取 / 已有引用 / 关键词) | Stage 2 文献检索 (S2 / OpenAlex / Crossref 并行 + DOI 去重) └→ 写 evidence_log.jsonl | Stage 3 真实性验证 (DOI / 跨源 → VERIFIED / LIKELY_REAL / UNVERIFIED) └→ 写 verification_log.jsonl | Stage 4 筛选排序 (相关性40% + 质量30% + 时效20% + 多样性10%) | *** 用户确认候选清单后才能继续 *** | Stage 5 引用插入 (LaTeX \cite{} / docx [N] 标记) | Stage 6 格式化输出 (IEEE / APA / Nature / Vancouver / GB/T 7714) | Stage 7 ┌─ 第 0 项: Gate 8 幻觉门控 (audit_no_hallucination.py) │ · 100% 重审 vs verification_log.jsonl 对账 │ · 任何 FAIL → exit 2，拒绝交付 └─ 第 1-5 项: 结构自检 (编号连续性 / 孤儿 / 格式 / 原文 diff / 预印本占比) ``` ### 真实性验证分级 + Gate 8 末端复核 | 等级 | 触发条件 | 处置 | |---|---|---| | `VERIFIED` | DOI 在 Crossref 命中，且标题相似度 ≥ 0.85、年份精确、首作者姓匹配 | 直接采用 | | `LIKELY_REAL` | 无 DOI 或 DOI 不匹配，但 Semantic Scholar 与 OpenAlex 都搜到该标题（相似度 ≥ 0.85） | 采用，在最终报告中**显式标注** | | `UNVERIFIED` | 上述都不通过 | **直接丢弃，不允许引用** | Stage 3 完成只是初筛。**真正的最终防线是 Stage 7 第 0 项的 Gate 8**—— 独立脚本 `audit_no_hallucination.py` 会对最终 bibliography 100% 重新验证（而不是抽样 20%），同时和 `verification_log.jsonl` 逐条对账。任何无溯源条目或 verdict 漂移立即触发 exit 2，整个交付流程中断。这是把"不编造"从提示词承诺升级成机器契约的关键一环。 ### 支持的引用格式 | 格式 | 主用领域 | 正文标记 | 排列方式 | |---|---|---|---| | **IEEE** | 工程、计算机、通信 | `[1]` `[1, 2]` `[1-3]` | 首次出现顺序 | | **APA 7th** | 心理学、教育学、社科 | `(Author, Year)` | 第一作者字母序 | | **Nature** | 自然科学顶刊 | 上标 `¹` `¹⁻³` | 首次出现顺序 | | **Vancouver** | 医学、生物医学 | `(1)` `(1-3)` | 首次出现顺序 | | **GB/T 7714-2015** | 中文期刊、硕博论文 | `[1]` | 引用顺序或著者-出版年 | 完整格式规范、模板、真实示例见 [`references/citation-formats.md`](references/citation-formats.md)。 ### SciPilot Skills 家族 | Skill | 状态 | 功能 | |---|---|---| | **scipilot-cite-skill** | v1.0.0 (本仓库) | 文献检索与引用插入 | | scipilot-writing-skill | [v1.0.0](https://github.com/Haojae/scipilot-writing-skill) | 学术写作与润色 + 写作质量证据链 | | scipilot-review-skill | 规划中 | AI 模拟审稿 | | scipilot-figure-skill | [v2.1.0](https://github.com/Haojae/scipilot-figure-skill) | 科研数据可视化顾问 + 绘制 | | scipilot-submit-skill | 规划中 | 投稿格式适配 | | scipilot-read-skill | 规划中 | 论文阅读与翻译 | 家族成员共享四条设计原则： 1. **AI 是副驾驶**：关键决策点等用户拍板 2. **真实性第一**：不编造任何学术信息 3. **一手文献驱动**：规则来自真实期刊规范，不靠"一般感觉" 4. **格式即法律**：同一论文格式必须绝对一致 ### 贡献欢迎 Issue 报告 bug、Feature Request、新引用格式贡献。提 PR 前请： 1. 确保 `scripts/utils.py`、`scripts/format_citation.py` 的内嵌测试能跑通 2. 新增格式需同时更新 `assets/format_templates/<style>.json` 和 `references/citation-formats.md` 3. SKILL.md 改动控制在 500 行内，详细内容放 `references/` ### 许可证 [MIT](LICENSE) © 2026 Haojae --- ## English ### Overview `scipilot-cite-skill` solves one of the most error-prone (and integrity-sensitive) parts of academic writing — **citations**. Two input modes: - **Mode A — manuscript mode**: read a `.tex` or `.docx` paper and insert citations into the right sections, with a References list at the end - **Mode B — snippet mode**: paste a paragraph or a viewpoint, get back a verified reference list that supports the claim (no document modification) Both modes parallel-search recent high-citation papers across **Semantic Scholar + OpenAlex + Crossref**, run each candidate through **DOI resolution + multi-source cross-check**, drop everything that fails verification, and output in your chosen style (**IEEE / APA 7 / Nature / Vancouver / GB/T 7714**). **Stage 0 actively asks 12 parameters**: input mode, count, citation style, year range, target sections, must-cite DOIs, venue preferences, **journal tier** (JCR Q1 / 中科院 1-2 区), **author preferences**, **author-affiliation preferences**, preprint policy, extra keywords. No silent defaults. **Core guarantees:** - Never fabricates a reference — failed candidates are discarded, not invented - Reference numbers strictly follow first-occurrence order, no gaps, no orphans - Your manuscript prose stays byte-identical except for the added markers and References - **Physical hallucination gate** — every entry in the delivered bibliography is re-audited 100% before delivery; anything without a provenance trail is blocked ### Gate 8: Physical Hallucination Audit / 幻觉门控 Most LLM citation assistants write "do not fabricate" into the system prompt and pray. That breaks down under long contexts, partial API failures, or user pressure — the typical failure is the **real-journal-with-fake-page-numbers / real-author-with-fake-DOI hybrid** that fools even expert reviewers. scipilot-cite-skill promotes "never fabricates" from a **slogan** to a **machine-checkable contract**: ``` search_papers.py → writes evidence_log.jsonl (every API response logged) ↓ verify_paper.py → writes verification_log.jsonl (every verdict logged) ↓ LLM prepares final_papers.json (after user sign-off in Stage 4) ↓ audit_no_hallucination.py — independent end-of-pipeline audit: · Re-queries 100% against live Crossref / S2 / OpenAlex · Reconciles every entry against verification_log.jsonl · Detects verdict drift (log says VERIFIED but live says UNVERIFIED) · Any orphan / log miss / drift → exit code 2 → delivery REFUSED ``` **Three concrete hallucination modes — all physically blocked:** | Mode | How Gate 8 catches it | |---|---| | LLM adds a paper from memory without searching | No matching `paper_id` in `verification_log.jsonl` → FAIL | | Stale or wrong API metadata accepted earlier | Live re-query at audit time returns 404 / mismatch → FAIL | | Real journal name + fabricated page or volume | DOI resolution returns the true metadata; comparison flags inconsistency → MISMATCH → FAIL | The red `Hallucination Audited: Gate 8` badge at the top of this README is not decoration — it points to `scripts/audit_no_hallucination.py`, which is the code that actually runs, actually exits with code 2, and actually refuses to deliver if any citation lacks provenance. ### Stage 0: active questioning (12 parameters) Most AI citation tools fill in defaults and start searching, leaving the user surprised at what comes back. `scipilot-cite-skill` makes Stage 0 a hard alignment step — no parameter, no search. | Category | Asked | Notes | |---|---|---| | Input mode | Mode A (paper) / Mode B (snippet) | Asked first | | Basics | target file / count / citation style | Always asked | | Search scope | year range / preprint policy | Defaults disclosed | | Target sections | which sections to cite into (Mode A only) | Defaults to auto-detect | | Quality / preferences | must-cite DOIs / venue preferences / **journal tier** / **author preferences** / **author affiliations** / extra keywords | User may skip per item | Once collected, the Skill **repeats every parameter back** and waits for an explicit "go". ### Key Features | Dimension | Implementation | |---|---| | Data sources | Semantic Scholar (200M+ papers), OpenAlex (250M+), Crossref (150M+ authoritative DOIs) | | Retrieval | ThreadPool parallel across 3 APIs, sort by citation count, DOI-primary dedup + fuzzy title dedup | | Authenticity tiers | `VERIFIED` (DOI hit) / `LIKELY_REAL` (cross-source) / `UNVERIFIED` (dropped) | | **Hallucination gate** | **Stage 7 Step 0: `audit_no_hallucination.py` blocking run; FAIL refuses delivery** | | Evidence ledger | `evidence_log.jsonl` + `verification_log.jsonl` written by scripts only; LLM has no write path | | Citation styles | IEEE / APA 7 / Nature / Vancouver / GB/T 7714-2015 + BibTeX export | | Document formats | LaTeX `.tex` (`\cite{}` + `thebibliography` or BibTeX), Word `.docx` (format-preserving) | | Workflow | 8 sequential stages, each gated by quality checks | | Dependencies | `requests` + `python-docx` + `python-Levenshtein`, **all APIs are free**, no key required | ### Installation #### Option A: Let Claude Code / Codex install it (recommended) Open Claude Code or Codex in your terminal and just type: ``` Please install this Skill for me: https://github.com/Haojae/scipilot-cite-skill.git ``` The agent will `git clone` into the right directory (`~/.claude/skills/` or `~/.codex/skills/`) and prompt you for the Python deps. #### Option B: Manual clone ```bash git clone https://github.com/Haojae/scipilot-cite-skill.git ~/.claude/skills/scipilot-cite-skill pip install -r ~/.claude/skills/scipilot-cite-skill/requirements.txt ``` Codex users: target `~/.codex/skills/`. Cursor users: target `.cursor/skills/`. #### Option C: Download ZIP 1. Click `Code` → `Download ZIP` on the GitHub page 2. Extract into `~/.claude/skills/scipilot-cite-skill/` 3. `pip install -r requirements.txt` ### Usage #### 1. Natural language trigger Inside Claude Code, plain prose in English or Chinese: **Mode A — insert into a manuscript**: ``` Add 15 references to my paper.tex in IEEE format, only into the Introduction and Related Work sections. ``` ``` Read thesis.tex, add 10 citations in GB/T 7714 format, prefer journals in JCR Q1, exclude preprints. ``` **Mode B — support a viewpoint (new)**: ``` I have this paragraph and want supporting references: "Diffusion-based methods have made notable progress in medical image synthesis, mitigating small-sample limitations." Find 8 recent (2022-2026) high-citation papers in IEEE format, prefer work from MIT / Stanford / Tsinghua. ``` ``` Is this claim well-supported in the literature? "RLHF significantly reduces harmful outputs of LLMs." Find 5 citations from Nature / Science / NeurIPS. ``` The Skill confirms all 12 parameters in Stage 0 (see above) before running search → verify → output. **It waits for your sign-off at every decision point.** #### 2. Standalone CLI Each script also runs on its own: ```bash # Parallel multi-source search python scripts/search_papers.py "diffusion model" --limit 10 --json > papers.json # Verify a real DOI python scripts/verify_paper.py doi 10.1038/s41586-024-07421-0 \ --title "Detecting hallucinations..." --year 2024 # Format one entry echo '{"title":"...","authors":["..."],"year":2024,"venue":"...","doi":"..."}' | \ python scripts/format_citation.py --style ieee --number 1 # End-to-end LaTeX insertion python scripts/insert_citations_latex.py paper.tex papers.json --style ieee # End-to-end Word insertion python scripts/insert_citations_docx.py paper.docx papers.json --style nature ``` ### Workflow (8 stages) ``` Stage 0 Parameter collection (count / style / years / preprint policy / extras) | Stage 1 Manuscript analysis (sections / existing cites / keywords) | Stage 2 Search (S2 / OpenAlex / Crossref in parallel, dedup by DOI) └→ writes evidence_log.jsonl | Stage 3 Verification (DOI / cross-check -> VERIFIED / LIKELY_REAL / UNVERIFIED) └→ writes verification_log.jsonl | Stage 4 Ranking (relevance 40 + quality 30 + recency 20 + diversity 10) | *** Wait for user confirmation of candidate list *** | Stage 5 Insertion (LaTeX \cite{} / docx [N] markers) | Stage 6 Format output (IEEE / APA / Nature / Vancouver / GB/T 7714) | Stage 7 ┌─ Step 0: Gate 8 hallucination audit (audit_no_hallucination.py) │ · 100% live re-verify + reconcile vs verification_log.jsonl │ · ANY fail → exit 2, delivery REFUSED └─ Steps 1-5: structural checks (numbering / orphans / format / diff / preprint mix) ``` ### Authenticity pipeline (3 tiers) + Gate 8 final audit | Tier | Trigger | Handling | |---|---|---| | `VERIFIED` | DOI hits Crossref; title similarity ≥ 0.85, exact year, first-author surname match | Accepted silently | | `LIKELY_REAL` | No DOI or DOI mismatch, but both Semantic Scholar and OpenAlex return the same title (similarity ≥ 0.85) | Accepted, **explicitly flagged** in the final report | | `UNVERIFIED` | None of the above | **Discarded, not citable** | Stage 3 is only an initial filter. **The real final defense is Gate 8 at Stage 7 Step 0** — the standalone `audit_no_hallucination.py` re-verifies 100% of the final bibliography (not a 20% sample) and reconciles every entry against `verification_log.jsonl`. Any orphaned entry or verdict drift triggers exit code 2, and the whole delivery pipeline stops. That is what turns "never fabricates" from a prompt-level promise into a machine-enforced contract. ### Supported citation styles | Style | Discipline | In-text marker | Ordering | |---|---|---|---| | **IEEE** | Engineering, CS, communications | `[1]` `[1, 2]` `[1-3]` | First occurrence | | **APA 7th** | Psychology, education, social sciences | `(Author, Year)` | Alphabetical by first author | | **Nature** | Top natural-science journals | Superscript `¹` `¹⁻³` | First occurrence | | **Vancouver** | Medicine, biomedicine | `(1)` `(1-3)` | First occurrence | | **GB/T 7714-2015** | Chinese journals, theses | `[1]` | First occurrence or author-year | Full specs, templates and real examples in [`references/citation-formats.md`](references/citation-formats.md). ### SciPilot Skills family | Skill | Status | Purpose | |---|---|---| | **scipilot-cite-skill** | v1.0.0 (this repo) | Reference discovery and insertion | | scipilot-writing-skill | [v1.0.0](https://github.com/Haojae/scipilot-writing-skill) | Writing & polishing + writing-quality evidence chain | | scipilot-review-skill | Planned | AI peer-review simulation | | scipilot-figure-skill | [v2.1.0](https://github.com/Haojae/scipilot-figure-skill) | Visualization advisor + renderer | | scipilot-submit-skill | Planned | Submission formatting | | scipilot-read-skill | Planned | Paper reading and translation | All members share four design principles: 1. **AI is a copilot** — wait for user sign-off at every decision point 2. **Authenticity first** — never fabricate academic information 3. **Primary-source driven** — rules come from real journal style guides 4. **Format is law** — one paper, one consistent style ### Contributing Bug reports, feature requests, and new-style contributions are welcome. Before opening a PR: 1. Ensure the embedded smoke tests in `scripts/utils.py` and `scripts/format_citation.py` still pass 2. New styles must update both `assets/format_templates/<style>.json` and `references/citation-formats.md` 3. Keep SKILL.md under 500 lines; long-form content belongs in `references/` ### License [MIT](LICENSE) © 2026 Haojae ### Dependencies ``` requests>=2.31.0 python-docx>=1.1.0 python-Levenshtein>=0.23.0 ``` Python 3.9+ recommended.

README des Repositorys wird geladen…

Skills in diesem Repository

Sonstige Hochschullehrer

1 Berufskategorien · 100% klassifiziert

Skill

Beruf

Beschreibung

Aktualisiert

scipilot-cite-skill

Sonstige Hochschullehrer

SciPilot Skills 家族的文献检索与引用插入技能。支持两种输入模式—— 模式 A：读取用户的 Word(.docx) 或 LaTeX(.tex) 论文，在正文对应章节插入引用；模式 B：接收用户给出的一段话/一个观点/一个论断，输出能支撑它的参考文献列表（不修改文档）。两种模式都通过 Semantic Scholar、OpenAlex、Crossref 三源并行检索真实文献，DOI + 多源交叉验证真实性，然后按用户指定格式（IEEE/APA/…

Quellsprache: Chinesisch

2026-06-15

Es werden 1 von 1 gesammelten Skills angezeigt.