Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/tomevault-io/skills-registry --skill research-profile명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | research-profile |
| description | > Use when this capability is needed. |
Before running any capability, load the user's research profile.
Location: ~/.openclaw/workspace/research-claw-config.md
If this file does not exist, use these defaults silently and mention at the end:
💡 想定制推荐兴趣?试试说「更新我的研究画像」
# Default profile (used when no config found)
research_direction: "Large language models, reinforcement learning, agentic AI"
seed_papers: []
keywords:
- large language models
- reinforcement learning
- agentic AI / AI agents
- retrieval-augmented generation
- multimodal models
whitelist_authors: []
learned_preferences:
accept: []
reject: []
Config fields reference:
research_direction — free-text description of the user's research focusseed_papers — list of arXiv IDs the user considers gold-standard referenceskeywords — interest topics used for Paper Scout search querieswhitelist_authors — researcher names to prioritize in recommendationslearned_preferences.accept — keywords/topics user has explicitly likedlearned_preferences.reject — keywords/topics user has skipped or dislikedGoal: Maintain the user's research preference profile and render it as a visual HTML page.
Triggers: 更新我的研究画像 · 我的研究画像 · research profile · auto-learn (from Paper Scout feedback)
Location: ~/.openclaw/workspace/research-claw-config.md
# ResearchClaw Config
# Auto-maintained by the agent. You can also edit manually.
research_direction: >
PhD researcher in large reasoning models and agentic memory systems.
Focus on RL-based training, long-context reasoning, and retrieval-augmented agents.
seed_papers:
- 2503.19823 # AutoRefine
- 2412.XXXXX # MemOCR
- 2502.XXXXX # ReMemR1
keywords:
- large language models
- reinforcement learning
- agentic memory
- long-context reasoning
- retrieval-augmented generation
- multimodal agents
whitelist_authors:
- Yaorui Shi
- An Zhang
- Xiang Wang
learned_preferences:
accept:
- RL-based reasoning
- verifiable rewards
View profile (我的研究画像):
🧠 你的研究画像
方向: {research_direction (first sentence)}
关键词: {keywords joined by · }
种子论文: {N} 篇
关注作者: {whitelist_authors joined by , }
偏好: +{accept topics} / −{reject topics}
Update profile (更新我的研究画像 [any description]):
Auto-learn (triggered by feedback on Paper Scout results):
learned_preferences.accept or .rejecttopic_stats by incrementing count for relevant topicsTemplate location: {SKILL_DIR}/templates/research-profile.html
read| Placeholder | Content |
|---|---|
{{USER_NAME}} | User's name from config or "Researcher" |
{{USER_TITLE}} | User's title/affiliation if known |
{{RESEARCH_DIRECTION}} | Full research direction text |
{{LAST_UPDATED}} | Today's date |
{{KEYWORD_COUNT}} | Total number of keywords |
{{KW_1}} … {{KW_10}} | Keyword names (fill up to 10) |
{{SEED_COUNT}} | Number of seed papers |
{{SEED_ID_1}}, {{SEED_TITLE_1}} | First seed paper ID + title |
{{SEED_ID_2}}, {{SEED_TITLE_2}} | Second seed paper ID + title |
{{SEED_ID_3}}, {{SEED_TITLE_3}} | Third seed paper ID + title |
{{AUTHOR_1}} … {{AUTHOR_5}} | Whitelist author names |
{{TOPIC_1}} … {{TOPIC_5}} | Top topic names |
{{CNT_1}} … {{CNT_5}} | Paper counts per topic |
{{PCT_1}} … {{PCT_5}} | Percentage per topic |
{{TOTAL_PAPERS}} | Total papers across all topics |
{{PREF_ACCEPT_1}}, {{PREF_ACCEPT_2}}, {{PREF_ACCEPT_3}} | Accept preference strings |
{{PREF_REJECT_1}}, {{PREF_REJECT_2}} | Reject preference strings |
~/.openclaw/workspace/research-claw-output/research-profile.html🧠 研究画像已更新 → ~/.openclaw/workspace/research-claw-output/research-profile.html| Error | Handling |
|---|---|
| arXiv API returns empty results | Retry once with broader query; if still empty, note "arXiv API temporarily unavailable" |
| PDF tool times out | Fall back to abstract-only mode; note [Abstract only — PDF timeout] in the note |
| PDF tool returns error for a paper | Try fetching https://ar5iv.labs.arxiv.org/html/{ARXIV_ID} as HTML fallback |
| Config file missing | Use defaults silently; add a note at end: "💡 想定制?说「更新我的研究画像」" |
| Reading list JSON missing or malformed | Start fresh with an empty list; inform user: "未找到现有列表,已新建空列表" |
| Template file not found | Report the expected path and ask user to check installation |
| No papers in last 3 days | Extend to 7 days, note it: "(近3天论文较少,已扩展至7天)" |
| Fewer than 3 read papers for Idea Generator | Proceed anyway, but note the limitation |
| User provides PDF/DOI instead of arXiv | Try to extract arXiv ID from DOI or search arXiv by title |
This section applies to Capabilities 2, 3, and 4.
The skill directory (where templates live) is the folder containing this SKILL.md file.
Typical path: ~/.openclaw/skills/research-claw/
Templates are at: ~/.openclaw/skills/research-claw/templates/
If you cannot determine the skill directory, use exec to find it:
find ~/.openclaw/skills -name "paper-note.html" 2>/dev/null | head -1
Default: ~/.openclaw/workspace/research-claw-output/
Create if needed:
mkdir -p ~/.openclaw/workspace/research-claw-output
The user can override the output directory by setting output_dir in their config.
read tool{{PLACEHOLDER}} → value#N/A or an empty string0write toolNever leave unfilled {{PLACEHOLDER}} tags in the output HTML.
Source: AlphaLab-USTC/ResearchClaw — distributed by TomeVault.