# Paper Expert Generator > Generate a specialized domain-expert research agent modeled on PaperClaw architecture. Use this skill when a user wants to create an AI agent that can automatically search, filter, summarize, and evaluate academic papers in a specific research field. Trigger phrases include help me create a paper tracking agent for my field, I want an agent to monitor latest papers in bioinformatics, build me a paper review agent for computer vision, create a PaperClaw-style agent for my domain, generate a domain-specific paper expert agent. The generated agent is a complete OpenClaw agent with all required skills (arxiv-search, semantic-scholar, paper-review, daily-search, weekly-report) fully adapted for the target domain. - Skill: `gabrielmoreira/paper-expert-generator` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add gabrielmoreira/paper-expert-generator` - Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/paper-expert-generator/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/gabrielmoreira/paper-expert-generator --- # Paper Expert Generator Generate a complete, ready-to-use domain-specific paper expert agent by adapting the PaperClaw architecture for any research field. ## Workflow ### Step 1: Domain Interview Collect these details from the user before generating anything. Ask conversationally – do not dump all questions at once. Start with the most critical ones: **Critical (ask first):** 1. **Research domain** – Primary field (e.g., "bioinformatics", "quantum computing", "computer vision") 2. **Core topics** – Specific sub-areas or problems (e.g., "protein folding, drug discovery, single-cell sequencing") 3. **Key methods/techniques** – Central methodologies (e.g., "transformers, GNN, diffusion models, RL") **Important (ask second):** 4. **Evaluation priorities** – What dimensions matter most for paper quality in this domain? 5. **Exclusion topics** – What should be filtered out? (e.g., "finance, social media, NLP") 6. **Output location** – Where to create the agent? (default: `~/agents//`) **Optional (ask only if needed):** 7. **Notification channel** – Feishu/Lark webhook URL for push notifications 8. **LLM config** – API base URL, model name, API key (default: same as PaperClaw models.json) 9. **Schedule timezone** – Default is `Asia/Singapore` Infer reasonable defaults for anything not provided and confirm before proceeding. ### Step 2: Build Keyword Library Construct a structured keyword library from the domain interview. Aim for: - **Core queries** (3–5): Direct topic+method combinations for arXiv `ti:` searches - **Method queries** (3–5): Method+application combinations - **Application queries** (2–3): Use-case-specific terms - **Exclusion keywords** (3–6): Out-of-scope terms to filter See `references/domain-adaptation-guide.md` Section 1 for keyword examples across 8 common domains. ### Step 3: Design Evaluation Rubric Design 4 domain-specific scoring dimensions (each scored 1–10) that replace PaperClaw's SciML dimensions (`engineering_value`, `architecture_innovation`, `theoretical_contribution`, `result_reliability`). The scoring formula is unchanged: ``` final_score = base_score × 0.9 + impact_score × 0.1 base_score = (dim1 + dim2 + dim3 + dim4) / 4 impact_score = date_citation_adjustment(citations, age_months) ``` See `references/domain-adaptation-guide.md` Section 2 for rubric examples by domain. ### Step 4: Generate Agent Files Run the scaffolding script to create the directory structure: ```bash python ~/.comate/skills/paper-expert-generator/scripts/init_domain_agent.py \ --domain "" \ --output "" \ --paperclaw-skills "" ``` Example: ```bash python ~/.comate/skills/paper-expert-generator/scripts/init_domain_agent.py \ --domain "bioinfo-ml" \ --output ~/agents/bioinfo-ml \ --paperclaw-skills /work/work/PaperClaw/skills ``` Generated structure: ``` / ├── agent/ │ ├── AGENT.md ← write domain content here │ ├── models.json ← pre-filled from template │ └── schedules.json ← pre-filled from template ├── skills/ │ ├── arxiv-search/ ← copy from PaperClaw (needs keyword update) │ ├── semantic-scholar/ ← copy from PaperClaw (no changes needed) │ ├── paper-review/ ← copy from PaperClaw (needs rubric update) │ ├── daily-search/ ← copy from PaperClaw (minor text update) │ └── weekly-report/ ← copy from PaperClaw (minor text update) └── workspace/ └── evaluated_papers.json ← initialized empty ``` ### Step 5: Write AGENT.md Use `assets/templates/AGENT.md.template` as the base. The AGENT.md must include: 1. **Role Definition** – Domain expert persona with specific depth. Replace SciML expertise with domain-specific expertise (key algorithms, theoretical foundations, benchmark datasets, top venues/conferences). 2. **Keyword Library** – Paste structured keywords from Step 2. 3. **Four Core Tasks** (preserve exact structure from PaperClaw): - **Task 1 (Paper Research)**: Download PDF → write `summary.md` answering 10 domain-adapted questions - **Task 2 (Paper Evaluation)**: 4-dimension scoring → write `scores.md` → update `metadata.json` → update registry - **Task 3 (Daily Search)**: Cron trigger → `daily_paper_search.py --top 3` → dedup → trigger Task 1+2 - **Task 4 (Weekly Report)**: Cron trigger → `generate_weekly_report_v2.py` → push notification 4. **Mandatory `` Reasoning** – Required in Task 2 evaluation. 5. **Dedup Gate** – Always check `evaluated_papers.json` before starting paper review. See `references/agent-template-guide.md` for the full AGENT.md authoring guide. ### Step 6: Adapt Skill SKILL.md Files Minimal adaptation needed – Python scripts are domain-agnostic: | Skill | Required changes to SKILL.md | |-------|------------------------------| | `arxiv-search` | Replace the keyword list with domain keywords from Step 2 | | `paper-review` | Replace 4 scoring dimensions + update the 10 summary questions | | `daily-search` | Update domain name in task description text | | `weekly-report` | Update domain name in report title | | `semantic-scholar` | No changes needed | ### Step 7: Configure models.json and schedules.json **models.json**: Edit `agent/models.json`, fill in: - `baseUrl`: LLM API endpoint - `apiKey`: API key placeholder - `id` and `name`: Model identifier **schedules.json**: Default schedule is pre-filled. Adjust `tz` field if not in Singapore timezone. ### Step 8: Validate and Deliver Checklist before presenting results: - [ ] `AGENT.md` has domain role, keywords, 4 tasks, rubric - [ ] `paper-review/SKILL.md` has domain scoring dimensions + 10 adapted questions - [ ] `arxiv-search/SKILL.md` has domain keyword list - [ ] `models.json` has correct structure (API key placeholder) - [ ] `workspace/evaluated_papers.json` initialized as `[]` - [ ] All 5 skill directories exist Then present the output summary (see next section). ## Output Summary Format Always deliver this summary after generation: ```markdown ## Generated Agent: Paper Expert **Domain**: **Location**: `` **Model**: ### Keyword Library ( total queries) **Core**: , , **Methods**: , **Exclusions**: , , ... ### Evaluation Rubric | Dimension | Score Weight | Measures | |-----------|-------------|---------| | | 25% | ... | | | 25% | ... | | | 25% | ... | | | 25% | ... | ### Schedule - Daily search: `0 20 * * *` () - Weekly report: `0 10 * * 0` () ### Quick Start 1. Open OpenClaw → select agent from `/agent/` 2. Set API key in `agent/models.json` 3. Test: "Search for recent papers on " 4. Or wait for first daily trigger at 20:00 ``` ## References - `references/domain-adaptation-guide.md` – Keyword and rubric examples for 8 common domains - `references/agent-template-guide.md` – Full AGENT.md authoring guide with annotated sections - `assets/templates/AGENT.md.template` – Base template for the generated AGENT.md - `assets/templates/models.json` – Base models config template - `assets/templates/schedules.json` – Base schedules config template