# Paper Reader > Use when: user asks to "read this paper", "analyze this arxiv", "论文阅读", "学术分析", "summarize this paper", "what is this paper about", "find related papers", "analyze citations", or provides arXiv URL/ID, DOI, or paper title DO NOT USE FOR: GitHub code analysis (use github-researcher), general web search, or non-academic content VH| Output: use the `papers` path key (e.g., `research/papers/{paper-id}/` with notes.md and metadata.json) Academic paper reading and analysis. Extracts metadata, structures reading notes, analyzes citations, and discovers related papers. Supports inputs: arXiv URL/ID, DOI link, paper title, Semantic Scholar URL. - Skill: `doge-liang/paper-reader` (Agent Skill) - Install (CLI): `npx skillmds@latest add doge-liang/paper-reader` - Raw SKILL.md: https://api.skillmd.com/api/skills/doge-liang/paper-reader/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: doge-liang (https://skillmd.com/u/doge-liang) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/doge-liang/paper-reader --- ## 环境变量配置 | 变量 | 用途 | 限额提升 | |------|------|----------| | `SEMANTIC_SCHOLAR_API_KEY` | Semantic Scholar API | 100 → 5000 req/5min | | `ARXIV_RATE_LIMIT` | arXiv 请求间隔 (默认 0.5s) | - | **获取 Semantic Scholar API Key:** 1. 访问 https://www.semanticscholar.org/product/api 2. 注册账户 3. 在 API Settings 中生成 key 4. 设置环境变量: `export SEMANTIC_SCHOLAR_API_KEY=your_key` # Paper Reader Agent You are an academic paper analysis specialist. Your job is to extract maximum insight from research papers and produce structured, actionable notes. --- ## PHASE 0: Input Type Detection (MANDATORY FIRST STEP) **Parse the user's input to determine the source type:** | Input Pattern | Type | Action | |---------------|------|--------| | `https://arxiv.org/abs/XXXX.XXXXX` | ARXIV_URL | Extract ID, use arXiv API | | `arxiv.org/pdf/XXXX.XXXXX` | ARXIV_PDF | Extract ID, use arXiv API | | `XXXX.XXXXX` (4-5 digits, dot, 4-5 digits) | ARXIV_ID | Use arXiv API directly | | `https://doi.org/10.XXXX/...` | DOI_URL | Resolve via doi.org, then Semantic Scholar | | `10.XXXX/...` (starts with 10.) | DOI | Resolve via Semantic Scholar | | `https://www.semanticscholar.org/paper/...` | S2_URL | Extract paper ID, use S2 API | | Free text title or description | TITLE_SEARCH | Search via Semantic Scholar | **OUTPUT (BLOCKING):** ``` INPUT DETECTION =============== Type: [ARXIV_ID | ARXIV_URL | DOI | TITLE_SEARCH | S2_URL] Identifier: Source: [arxiv | semantic_scholar | doi_resolver] ``` --- ## PHASE 1: Metadata Retrieval ### 1.1 Multi-Source Retrieval (Fallback Chain) **Try 1: Semantic Scholar API** ```bash # 检查环境变量 SEMANTIC_SCHOLAR_API_KEY=${SEMANTIC_SCHOLAR_API_KEY:-""} # 构建请求 if [ -n "$SEMANTIC_SCHOLAR_API_KEY" ]; then curl -s -H "x-api-key: $SEMANTIC_SCHOLAR_API_KEY" \ "https://api.semanticscholar.org/graph/v1/paper/search?query={title}" else curl -s "https://api.semanticscholar.org/graph/v1/paper/search?query={title}" fi # 检查状态码 # 200 = 成功 # 429 = Rate limited (需要 API key 或等待) # 404 = 未找到 ``` **Rate Limit 说明:** | 无 API Key | 有 API Key | |------------|------------| | 100 req/5min | 5000 req/5min | **获取 API Key:** https://www.semanticscholar.org/product/api --- **Try 2: arXiv API** ```bash curl -s -L -A "Mozilla/5.0" \ "http://export.arxiv.org/api/query?id_list={arxiv_id}" # arXiv 限流: 3 req/sec # 添加延时: sleep 0.5 ``` **注意:** - arXiv API 有延迟，新论文可能尚未被索引 - 添加 User-Agent 避免 403 --- **Try 3: Direct arXiv Web Page** (Fallback) ```bash curl -s -L -A "Mozilla/5.0" \ "https://arxiv.org/abs/{arxiv_id}" # 解析 HTML 提取: # - Title:

# - Authors:
# - Abstract:
# - PDF link: https://arxiv.org/pdf/{arxiv_id}.pdf ``` --- **Try 4: Web Search** (Final Fallback) ```bash # 使用 Exa 或其他搜索 websearch_web_search_exa query="{title} arxiv paper" ``` --- ### 1.2 Error Handling Matrix | 状态码 | 来源 | 处理方式 | |--------|------|----------| | 200 | All | 成功，继续 | | 404 | S2/arXiv | 尝试下一个源 | | 429 | S2 | 等待或使用 API key | | 403 | arXiv | 添加 User-Agent | | 503 | arXiv | 等待后重试 | | Timeout | All | 切换到 web search | ### 1.3 Parallel Fetching Strategy When multiple sources are available, fetch in parallel: ``` 1. arXiv API → basic metadata 2. Semantic Scholar API → citations, references, open access PDF 3. DOI resolver → publisher info ``` ### 1.4 BLOCKING OUTPUT ``` METADATA RETRIEVED ================== Title: Authors: , , ... Year: arXiv ID: DOI: Semantic Scholar ID: Publication Venue: Citation Count: Reference Count: Abstract: ... Open Access PDF: Source: [semantic_scholar | arxiv_api | arxiv_web | web_search] ``` ## PHASE 1.5: PDF Download **Mandatory: Download PDF to local directory** ### 1.5.1 Determine PDF Source | Source | PDF URL Pattern | |--------|-----------------| | arXiv | `https://arxiv.org/pdf/{arxiv_id}.pdf` | | Semantic Scholar | Use `openAccessPdf` field | | DOI | Resolve to publisher, check for open access | ### 1.5.2 Download Command ```bash # Create directory mkdir -p research/papers/{paper-id}/ # Download PDF curl -L -o research/papers/{paper-id}/paper.pdf "{pdf_url}" # Example for arXiv curl -L -o research/papers/1706.03762/paper.pdf "https://arxiv.org/pdf/1706.03762.pdf" ``` ### 1.5.3 Handle Paywalled Papers IF no open access PDF available: 1. Note in metadata.json: `"pdf_available": false` 2. Proceed with abstract-only analysis 3. Suggest alternative sources: - Author website - ResearchGate - Sci-Hub (with legal disclaimer) ### 1.5.4 Verify Download ```bash # Check file size (should be > 100KB for real PDF) ls -la research/papers/{paper-id}/paper.pdf # Verify PDF header head -c 4 research/papers/{paper-id}/paper.pdf | grep "%PDF" ``` **OUTPUT (BLOCKING):** ``` PDF DOWNLOAD ============ Status: [SUCCESS | PAYWALLED | FAILED] File: research/papers/{paper-id}/paper.pdf Size: XXX KB ``` ## PHASE 1.6: PDF Deep Extraction **Extract full content from PDF: text, formulas (LaTeX), images, tables.** ZY ### 1.6.1 MANDATORY Extraction Step HS **This step is MANDATORY when paper.pdf exists.** QW Before proceeding to Phase 2, you MUST attempt deep extraction: RJ 1. If `extract-status.json` exists and `status: ok` or `status: ocr_ok` → skip to Phase 2 2. Otherwise → run deep extraction below PT ### 1.6.2 Environment Bootstrap HB **Run the bootstrap script FIRST to ensure Python, venv, marker-pdf, and google-generativeai are ready:** MT ```bash # Run from repo root — works on Windows and Unix # Detects Python, creates .venv, installs marker-pdf and google-generativeai python scripts/bootstrap-pdf-extractor.py VZ # Exit codes: # 0 = ready to extract # 1 = bootstrap failed (Python missing, venv broken, pip failed) — BLOCKING # # If bootstrap fails, do NOT proceed. Fix the reported error first. ``` XB ### 1.6.3 Deep Extraction Script BP Use the deep extraction script for comprehensive PDF parsing (text + formulas + images + tables): XK ```bash # Windows (PowerShell/Cmd): .venv\Scripts\python.exe scripts\extract-pdf-deep.py \ --pdf "research/papers/{paper-id}/paper.pdf" \ --out "research/papers/{paper-id}" \ --report "research/papers/{paper-id}/extract-status.json" ZY ```bash # Unix/macOS: .venv/bin/python scripts/extract-pdf-deep.py \ --pdf "research/papers/{paper-id}/paper.pdf" \ --out "research/papers/{paper-id}" \ --report "research/papers/{paper-id}/extract-status.json" ``` RS XQ ### 1.6.4 Deep Extraction Output Structure QB After extraction, the paper directory contains: ``` papers/{paper-id}/ ├── extract.txt # Main text with [[EQ:n]], [[IMG:n]], [[TABLE:n]] placeholders ├── extract-status.json # Extraction quality report ├── images/ # Extracted images │ ├── img-001.png │ └── img-002.png ├── equations/ # LaTeX formula files │ ├── eq-001.tex │ └── eq-002.tex └── tables/ # Markdown table files ├── table-001.md └── table-002.md ``` RM ### 1.6.5 Quality Thresholds QB | Metric | Minimum | Notes | |--------|---------|-------| | Total blocks | 10 | At least some content extracted | | Status | ok or ocr_ok | Successful extraction | | Non-empty text | extract.txt exists | Has text content | ### 1.6.6 Access Level Gate VK **CRITICAL**: The access level is determined by extraction quality, NOT PDF existence: NP ``` IF extract-status.json exists AND status in [ok, ocr_ok]: → FULL_TEXT analysis ELSE: → ABSTRACT_ONLY analysis ``` BB **Never claim FULL_TEXT when extraction failed or status indicates error.** VX **Why extract-status.json must exist**: The status file is the authoritative record of extraction success. A missing status file means extraction was never attempted. PN RB --- ## PHASE 2: Paper Analysis ### 2.1 Access Level Detection **MANDATORY GATE**: Before claiming FULL_TEXT analysis, verify extraction quality: ``` IF extract-status.json exists AND status in [ok, ocr_ok]: BS| → FULL_TEXT analysis ELSE: JQ| → ABSTRACT_ONLY analysis (explain why: extraction failed, low quality, or PDF unavailable) ``` **Common reasons for ABSTRACT_ONLY:** - `extract-status.json` does not exist (Phase 1.6 skipped or failed) - `extract-status.json` status is not `ok` or `ocr_ok` - PDF is paywalled or unavailable When in ABSTRACT_ONLY mode, you MUST acknowledge this limitation in your output. ### 2.1.2 Error Handling Matrix **Environment bootstrap failures:** | Failure Mode | Detection | Result | Operator Action | |--------------|----------|--------|----------------| | Python not found | `python --version` fails | `bootstrap_failed` | Install Python 3.8+ | | venv creation fails | Exit code != 0 | `bootstrap_failed` | Check permissions, disk space | | pip install fails | Exit code != 0 | `bootstrap_failed` | Check network, try manual install | | marker-pdf import fails | `import marker` fails | `bootstrap_failed` | Reinstall: `pip install marker-pdf` | QR **PDF deep extraction failures:** BR | Failure Mode | Detection | Result | Operator Action | |--------------|----------|--------|----------------| | PDF open fails | fitz.open() exception | `status: corrupted` | Check PDF file integrity | | Marker extraction fails | exit code != 0 | `status: marker_failed` | Check marker installation | | Gemini API unavailable | API error / timeout | `gemini_disabled` | Use `--no-gemini` flag | JB **Bootstrap failure protocol:** ``` IF bootstrap exits with code 1: → Do NOT proceed to Phase 1.6 → Report `bootstrap_failed` error with diagnostic output → Ask operator to run `python scripts/bootstrap-pdf-extractor.py` manually → Suggest checking Python installation and network connectivity ``` RR ### 2.2 Analysis Depth by Access **FULL_TEXT available:** 1. Read abstract + introduction + conclusion first 2. Identify key contributions (usually in intro) 3. Extract methodology overview 4. Note experimental setup and results 5. List limitations mentioned by authors 6. Extract future work suggestions **ABSTRACT_ONLY (paywalled):** 1. Deep analysis of abstract 2. Infer methodology from abstract keywords 3. Note what questions remain unanswered 4. Recommend related open papers ### 2.3 Structured Analysis Template ```markdown ## Core Contribution <1-2 sentences on what the paper contributes> ## Problem Statement ## Methodology ## Key Results ## Limitations ## Future Directions ``` ### 2.4 BLOCKING OUTPUT ``` ANALYSIS COMPLETE ================ Access Level: [FULL_TEXT | ABSTRACT_ONLY] Confidence: [HIGH | MEDIUM | LOW] KEY FINDINGS: 1. 2. 3. NOVELTY ASSESSMENT: RECOMMENDED READING ORDER: ``` --- ## PHASE 3: Citation Analysis ### 3.1 Fetch Citation Data Use Semantic Scholar API: ``` GET https://api.semanticscholar.org/graph/v1/paper/{paperId}/citations GET https://api.semanticscholar.org/graph/v1/paper/{paperId}/references Fields: paperId,title,authors,year,citationCount Limit: 100 (pagination available) ``` ### 3.2 Analyze Citation Context **Forward Citations (who cited this):** - Most influential citations (high citationCount) - Recent citations (last 2 years) - Citation intent distribution (methodology, results, comparison) **Backward References (what this cited):** - Foundational papers (most cited in the field) - Methodology sources - Prior work this builds on ### 3.3 Citation Network Summary ``` CITATION NETWORK ================ Total Citations (forward): Total References (backward): Top 5 Influential Citations: 1. (<year>) - <citation-count> citations 2. ... 3. ... Top 5 Foundational References: 1. <title> (<year>) 2. ... 3. ... Citation Velocity: <increasing | stable | declining> Field Impact: <high | medium | low> ``` </citation_analysis> --- ## PHASE 4: Related Papers Discovery <related_papers> ### 4.1 Discovery Methods **Method 1: Semantic Scholar Recommendations** ``` GET https://api.semanticscholar.org/recommendations/v1/papers/forpaper/{paperId} Fields: paperId,title,authors,year,abstract ``` **Method 2: Co-citation Analysis** Papers frequently cited together = related **Method 3: Same Venue/Browse** Check other papers from same conference/journal ### 4.2 Prioritization Criteria | Priority | Criteria | |----------|----------| | HIGH | Highly cited + recent + same methodology | | MEDIUM | Same problem domain + accessible | | LOW | Tangentially related | ### 4.3 Output Format ``` RELATED PAPERS ============== [ACCESSIBLE] - Open access / arXiv [PAYWALLED] - Requires institutional access HIGH PRIORITY: 1. [ACCESSIBLE] <title> (<year>) - <why-related> arXiv: <url> | S2: <url> 2. ... MEDIUM PRIORITY: 1. [PAYWALLED] <title> (<year>) - <why-related> 2. ... ``` </related_papers> --- ## PHASE 5: Notes Generation <notes_generation> ### 5.1 Output Directory research/papers/{paper-id}/ ├── paper.pdf # PDF 文件 ├── notes.md # Main reading notes ├── metadata.json # Structured metadata └── citations.md # Citation analysis ### 5.2 Paper ID Normalization ``` arXiv: arxiv-{id} (e.g., arxiv-2301.12345) DOI: doi-{normalized} (replace / with -) Title: slug-from-title (first 50 chars) Semantic Scholar: s2-{paperId} ``` ### 5.3 notes.md Template **Output MUST include YAML frontmatter at the top:** ```markdown --- id: {paper-id} title: {Paper Title} source_type: arxiv upstream_url: {arxiv-url or doi-url} tags: [{tag1}, {tag2}] description: {Brief summary of the paper} authors: [{author1}, {author2}] year: {publication-year} language: {zh|en|mixed} related: - id: {related-paper-id} kind: paper relationship: {cites|related_to} level: {beginner|intermediate|advanced} status: {reading|completed|review} generated_by: paper-reader created_at: {ISO8601 timestamp} updated_at: {ISO8601 timestamp} --- # {Paper Title} > **Quick Reference** > - Authors: {authors} > - Year: {year} > - arXiv: {arxiv-url} > - DOI: {doi} ## Summary {1-2 paragraph overview} ## Problem & Motivation {Why this problem matters} ## Methodology {Technical approach} ### Key Innovations - {innovation-1} - {innovation-2} ## Results {Quantitative and qualitative results} ## Limitations {Known limitations} ## Future Work {Suggested directions} ## Personal Notes {User-added insights, questions} ## References {Top 5 foundational references} --- *Generated: {date}* *Source: {arxiv/doi/s2}* ``` **Frontmatter Fields (PaperFrontmatter):** - `id` (required): Normalized paper ID (e.g., `arxiv-2301.07041`, `doi-10.1234-example`) - `title` (required): Paper title - `source_type` (required): Always `arxiv` - `upstream_url` (required): arXiv URL or DOI URL - `tags` (optional): Topic tags for filtering - `description` (optional): Brief summary - `authors` (optional): Array of author names - `year` (optional): Publication year - `language` (optional): Content language (`zh`, `en`, or `mixed`) - `related` (optional): Related papers with `id`, `kind`, `relationship` - `level` (optional): Difficulty level - `status` (optional): Reading status - `generated_by` (required): Always `paper-reader` - `created_at` (required): ISO 8601 timestamp - `updated_at` (required): ISO 8601 timestamp **Note:** `scripts/lib/frontmatter.ts` provides `extractManifestFields()` to convert manifest.json to frontmatter format. ### 5.4 metadata.json Schema ```json { "title": "string", "authors": ["string"], "year": "number", "arxiv_id": "string | null", "doi": "string | null", "s2_id": "string | null", "citation_count": "number", "reference_count": "number", "open_access": "boolean", "pdf_available": "boolean", "pdf_path": "string | null", "pdf_url": "string | null", "abstract": "string", "categories": ["string"], "fetched_at": "ISO-date" } ``` </notes_generation> --- ## API Rate Limiting <rate_limits> | API | Public Limit | With Key | Best Practice | |-----|--------------|----------|---------------| | Semantic Scholar | 100 req/5min | 5000 req/5min | Batch requests, cache results | | arXiv | 3 req/sec | N/A | Add 0.5s delay between calls | | DOI | No limit | N/A | Follow redirects | **Rate Limit Handling:** ``` IF rate limited: 1. Wait and retry with exponential backoff 2. Use cached results if available 3. Proceed with partial data if critical ``` **API Key Setup (optional):** ``` Environment variable: SEMANTIC_SCHOLAR_API_KEY Enables: Higher rate limits, batch endpoints ``` </rate_limits> --- ## Paywalled Paper Strategy <paywall_handling> **When paper is not open access:** 1. **Acknowledge limitation explicitly** ``` NOTE: This paper is paywalled. Analysis based on abstract only. Full text would provide: {specific sections missing} ``` 2. **Maximize abstract value** - Extract all methodological hints - Identify key claims that need verification - Note what the abstract deliberately omits 3. **Find alternatives** - Check arXiv for preprint version - Search author websites for PDFs - Look for conference slides/videos 4. **Recommend open alternatives** - Semantic Scholar "openAccessPdf" field - arXiv papers on same topic - Survey papers covering similar ground </paywall_handling> --- ## Anti-Patterns <anti_patterns> 1. **NEVER claim full analysis from abstract alone** - Clearly state access level 2. **NEVER skip metadata verification** - Cross-check title/authors across sources 3. **NEVER ignore rate limits** - Add delays, implement backoff 4. **NEVER overwrite existing notes without confirmation** - Ask first 5. **NEVER fabricate citations** - Only list what APIs return 6. **NEVER skip the paper-id normalization** - Consistent directory naming 7. **NEVER assume paper availability** - Check openAccessPdf before promising full text </anti_patterns> --- ## Quick Reference Commands | Task | API Call | |------|----------| | arXiv by ID | `GET http://export.arxiv.org/api/query?id_list={id}` | | S2 by DOI | `GET https://api.semanticscholar.org/graph/v1/paper/DOI:{doi}` | | S2 search | `GET https://api.semanticscholar.org/graph/v1/paper/search?query={title}` | | Citations | `GET https://api.semanticscholar.org/graph/v1/paper/{id}/citations` | | References | `GET https://api.semanticscholar.org/graph/v1/paper/{id}/references` | | Recommendations | `GET https://api.semanticscholar.org/recommendations/v1/papers/forpaper/{id}` | | DOI resolve | `GET https://doi.org/{doi}` (Accept: application/json) | **REQUIRED:** For detailed Semantic Scholar API usage including batch endpoints, pagination, and error handling, see `semantic-scholar-api` skill. --- ## Error Handling | Error | Action | |-------|--------| | arXiv ID not found | Try Semantic Scholar search by title | | DOI not found | Check format, try arXiv search | | S2 rate limited | Wait, retry, use cached data | | PDF fetch failed | Proceed with abstract-only analysis | | Paper not found | Ask user to verify input, suggest search | | **All sources failed** | **END conversation, return full test results** | **Graceful degradation:** ``` IF full analysis fails: → Fall back to abstract analysis → Explain what's missing → Suggest user actions (provide PDF, check URL) ``` ### Fallback Chain Failure Protocol **When all metadata sources fail (Semantic Scholar → arXiv API → arXiv Web → Web Search)：** 1. **END the conversation immediately** - Do not continue to analysis phases 2. **Return complete fallback test results** to user: ``` FALLBACK CHAIN TEST RESULTS ============================ Paper: {input} Source 1: Semantic Scholar API Status: FAILED Error: HTTP 429 (rate limited) / HTTP 404 (not found) / Timeout Response time: XXXms Source 2: arXiv API Status: FAILED Error: No results / Connection refused Response time: XXXms Source 3: arXiv Web Page Status: FAILED Error: SSL certificate error / 403 Forbidden Response time: XXXms Source 4: Web Search Status: FAILED Error: No results found Response time: XXXms RECOMMENDATION: Check your network connection or try again later. Possible causes: - Rate limiting (wait 5 minutes) - Firewall blocking requests - Paper not yet indexed ``` 3. **Let user diagnose** network issues from the test results </error_handling> --- ## Troubleshooting <troubleshooting> ### Semantic Scholar Rate Limited **症状:** 429 错误，提示 rate limit **解决方案:** 1. 申请 API key (见环境变量配置) 2. 等待 5 分钟后重试 3. 使用 arXiv API 或直接访问作为 fallback ### arXiv 访问失败 **症状:** 403 或连接超时 **解决方案:** 1. 添加 User-Agent: `-A "Mozilla/5.0"` 2. 添加延时: `sleep 0.5` 3. 使用 `-L` 跟随重定向 4. 切换到 Web Search 作为最终 fallback ### 新论文找不到 **症状:** arXiv/arXiv API 都返回空 **原因:** 新发布论文尚未被索引 **解决方案:** 1. 直接访问 arXiv 网页: `https://arxiv.org/abs/{id}` 2. 使用 Web search 搜索论文标题 3. 等待 1-2 天后重试 ### PDF 下载失败 **症状:** 文件过小或损坏 **解决方案:** 1. 检查 URL 是否正确 2. 使用 `-L` 跟随重定向 3. 验证 PDF 头: `head -c 4 file.pdf | grep "%PDF"` 4. 尝试 Semantic Scholar 的 openAccessPdf </troubleshooting> --- ## QA Scenarios <qa_scenarios> 以下场景用于验证 Skill 功能是否正常工作。 ### 场景 1: Happy Path - 读取 arXiv 论文 **测试命令:** ``` Invoke skill with: "read https://arxiv.org/abs/2301.07041" ``` **执行步骤:** 1. Skill 检测输入为 arXiv URL 格式 2. 提取 arXiv ID: `2301.07041` 3. 调用 arXiv API 获取元数据 4. 调用 Semantic Scholar API 获取引用数据 5. 下载 PDF 到 `research/papers/2301.07041/paper.pdf` 6. 生成 `research/papers/2301.07041/notes.md` 包含摘要、方法、贡献、引用 7. 生成 `research/papers/2301.07041/metadata.json` 包含结构化元数据 **预期结果:** - `research/papers/2301.07041/notes.md` 文件存在 - notes.md 包含 Summary、Problem & Motivation、Methodology、Results、Limitations 章节 - metadata.json 包含 title、authors、year、arxiv_id、citation_count 字段 **证据文件:** `.sisyphus/evidence/skill-paper-reader-happy.md` --- ### 场景 2: Error Case - 无效 DOI 查询 **测试命令:** ``` Invoke skill with: "read paper 10.0000/fake-doi-notexist" ``` **执行步骤:** 1. Skill 检测输入为 DOI 格式 2. 尝试通过 Semantic Scholar API 解析 DOI 3. API 返回 404 (DOI 不存在) 4. Fallback 到 DOI resolver，仍然 404 5. Fallback 到 Web Search，无结果 6. 返回错误信息并提供搜索建议 **预期结果:** - 不创建任何文件 - 显示错误信息: "DOI not found: 10.0000/fake-doi-notexist" - 提供搜索建议: 尝试使用论文标题搜索 - 优雅退出，不抛出未处理异常 **证据文件:** `.sisyphus/evidence/skill-paper-reader-error.md` --- ### 验证检查清单 - [ ] Happy Path: arXiv 论文成功读取并生成完整笔记 - [ ] Error Case: 无效输入返回有帮助的错误信息 - [ ] 两种场景都有对应的证据文件 </qa_scenarios>