What makes sciverse academic retrieval notable?

2.8K downloads. Trust evidence available

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agentCLAWHUBUnverified

sciverse academic retrieval

Retrieve academic papers by structured metadata, perform semantic chunk search for RAG, and read byte-range content for citation-grade scientific literature.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

2.8k

Updated

Oct 9, 2026

Version

0.14.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.8K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2.8K downloadsadoption · observed Oct 9, 2026
Latest release: 0.14.3release · observed Sep 20, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17cs0hvsmy3xp1z9jffmdc8rn86j73f:academic-retrieval

Install using `clawhub skill install s17cs0hvsmy3xp1z9jffmdc8rn86j73f:academic-retrieval` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/sciverse/academic-retrieval before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sciverse-academic-retrieval/snapshot"

Documentation

CLAWHUB

153,118 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: sciverse-academic-retrieval
slug: academic-retrieval
version: 0.14.3
description: Sciverse academic paper retrieval: structured metadata search, semantic chunk retrieval for RAG, and character-range content reading (offsets in Unicode code points). For agent workflows that need citation-grade scientific literature.
license: Apache-2.0
homepage: https://sciverse.space
---

# academic-retrieval

Sciverse academic paper retrieval: structured metadata search, semantic chunk retrieval for RAG, and character-range content reading (offsets in Unicode code points). For agent workflows that need citation-grade scientific literature.

## When to use

Trigger this skill when the user's request involves any of:

- Locating academic papers by structured criteria (authors, year, journal, subjects)
- Grounding answers in paper excerpts (RAG / citations)
- Expanding the original text around a known doc_id (more text before/after a chunk)

## Authentication

This skill requires the `SCIVERSE_API_TOKEN` environment variable
(obtain from https://sciverse.space). Optionally set `SCIVERSE_BASE_URL`
to override the default API base URL.

## Tools

### search_papers

Search academic papers by structured filters (title, authors, journal,
year, subjects, etc.).
Use when: "find Hinton's papers from 2020-2023", "Nature papers on
CRISPR".
Not for: natural-language Q&A retrieval (use semantic_search) or
full-text snippets (use read_content).
Returns: list of papers; each entry has unique_id (always present),
doc_id (only when full text exists), title, author, abstract,
publication_venue_name_unified, publication_published_year.

**Invoke**: `node scripts/search_papers.mjs '<JSON args>'`

### semantic_search

Natural-language semantic search returning relevant paper chunks for
RAG-style answering.
Use when: "How does Transformer attention work?", "What are recent
methods for protein structure prediction?".
Not for: precise field filtering (use search_papers) or fetching full
original text (use read_content).
Returns: list of chunks; each entry has chunk_id, doc_id, abstract,
chunk, score, title, offset.
Typical chain: semantic_search → pick chunk → read_content(doc_id,
offset).

**Invoke**: `node scripts/semantic_search.mjs '<JSON args>'`

### list_catalog

Returns the schema catalog for search_papers: every field name, type,
whether it's filterable / sortable, default-return status, human
description, and applicable FilterOperators.
Use when: "Which field do I filter by DOI?", "What values can
access_oa_status take?", "What's the right enum for metadata_type?".
Not for: actually searching papers (use search_papers / semantic_search).
Typical pattern: call once when first encountering Sciverse or facing
an ambiguous field need, then construct precise search_papers filters
from the returned schema.
Pass include_sample_values=true to also fetch top-20 values for
enum-like fields (OpenSearch terms aggregation, 24h cached).

**Invoke**: `node scripts/list_catalog.mjs '<

README.md

# academic-retrieval — ClawHub skill bundle

[![ClawHub](https://img.shields.io/badge/clawhub-academic--retrieval-brightgreen)](https://clawhub.ai/sciverse/skills/academic-retrieval)

ClawHub skill that gives any OpenClaw agent Sciverse academic-paper retrieval
capabilities (English | [中文](#中文说明)).

Published by **@sciverse** (slug `academic-retrieval`).

## Install

```bash
openclaw skills install academic-retrieval
```

## Configure

```bash
export SCIVERSE_API_TOKEN=sv-xxx       # obtain from https://sciverse.space
```

## Tools at a glance

| Tool | Purpose |
|---|---|
| `list_catalog` | Field introspection (call once to learn available fields + enum values) |
| `search_papers` | Structured metadata search over papers / authors / sources (set `collection`) |
| `semantic_search` | Natural-language semantic chunk retrieval (for RAG) |
| `read_content` | Character-range read of a paper's original text (offset/limit in Unicode code points) |
| `get_resource` | Fetch figure / table image bytes referenced inside `read_content` Markdown |

See `SKILL.md` for full agent-facing documentation.

## Direct invocation (bypass OpenClaw)

```bash
node scripts/semantic_search.mjs '{"query":"Transformer attention mechanism","top_k":3}'
```

## Relationship to the SDK

This skill is **complementary** to the `sciverse` packages on PyPI / npm:

- **This skill** — OpenClaw users only. Zero external deps (Node 18+ native fetch).
- **PyPI / npm SDK** — Any LLM agent framework (OpenAI, Anthropic, LangChain, LlamaIndex…).

## License

Apache-2.0

---

## 中文说明

OpenClaw 用户专用：通过 ClawHub 一键给 agent 加上 Sciverse 学术文献检索能力。

发布者 **@sciverse**，slug `academic-retrieval`。

### 安装

```bash
openclaw skills install academic-retrieval
```

### 配置

```bash
export SCIVERSE_API_TOKEN=sv-xxx   # 从 https://sciverse.space 控制台申请
# 可选：export SCIVERSE_BASE_URL=https://api-custom.sciverse.space
```

### 工具速览

| Tool | 用途 |
|---|---|
| `list_catalog` | 字段 introspection（首次接入调一次，学习可用字段和 enum 取值） |
| `search_papers` | 按结构化条件查 papers / authors / sources（用 `collection` 切换实体集合） |
| `semantic_search` | 自然语言语义检索文献片段（RAG 用） |
| `read_content` | 按 Unicode 码点区间读取文献原文片段 |
| `get_resource` | 取 `read_content` Markdown 中引用的图片字节流（多模态 RAG） |

agent 视角的完整文档见 `SKILL.md`（英文）。

### 直接调用（不通过 OpenClaw）

```bash
node scripts/semantic_search.mjs '{"query":"Transformer 注意力机制","top_k":3}'
```

### 与 SDK 的关系

本 skill 与 PyPI/npm 上的 `sciverse` 包是**互补**的：

- **本 skill**：OpenClaw 用户专用，零外部依赖（仅 Node 18+ native fetch）
- **PyPI/npm SDK**：任意 LLM Agent 框架（OpenAI / Anthropic / LangChain / LlamaIndex...）

_meta.json

{
  "ownerId": "kn74way11x0gjn6wpa8hcvyhvs85vmkj",
  "slug": "academic-retrieval",
  "version": "0.14.3",
  "publishedAt": 1789876890789
}

skill-card.md

## Description:

Sciverse academic paper retrieval supports structured metadata search, semantic chunk retrieval for RAG, citation relation lookup, and character-range content reading for scientific literature workflows.

This skill is ready for commercial/non-commercial use.

## Publisher:

[sciverse](https://clawhub.ai/user/sciverse)

### License/Terms of Use:

Apache-2.0

## Use Case:

Developers and research-focused agents use this skill to locate academic papers, retrieve grounded text snippets, inspect citation relationships, and fetch referenced figures or tables for literature review and RAG workflows.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Search queries, document IDs, and resource names are sent to Sciverse endpoints with the user's API token.

Mitigation: Use a token approved for the intended workspace and avoid submitting confidential research prompts or identifiers unless Sciverse terms and internal policy allow it.

Risk: Retrieved chunks, full text, citation relations, and images can be permission-limited, partial, or approximate for citation-grade use.

Mitigation: Check content accessibility and verify important claims or citations against the source publication before relying on downstream answers.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/sciverse/skills/academic-retrieval)
- [Sciverse homepage](https://sciverse.space)

## Skill Output:

**Output Type(s):** [text, markdown, JSON, images, configuration]

**Output Format:** [JSON responses containing paper metadata, semantic chunks, Markdown text fragments, citation relation lists, or base64-encoded image resources]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires a Sciverse API token; Node 18+ scripts call Sciverse endpoints and print machine-readable JSON.]

## Skill Version(s):

0.14.3 (source: server release metadata and SKILL.md frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

manifest.json

{
  "name": "sciverse-academic-retrieval",
  "version": "0.14.3",
  "slug": "academic-retrieval",
  "description": "Sciverse academic paper retrieval: structured metadata search, semantic chunk retrieval for RAG, and character-range content reading (offsets in Unicode code points). For agent workflows that need citation-grade scientific literature.",
  "runtime": "node>=18",
  "license": "Apache-2.0",
  "homepage": "https://sciverse.space",
  "env": [
    {
      "name": "SCIVERSE_API_TOKEN",
      "required": true,
      "description": "Sciverse API Token (obtain from https://sciverse.space)."
    },
    {
      "name": "SCIVERSE_BASE_URL",
      "required": false,
      "default": "https://api.sciverse.space",
      "description": "Override the default API base URL (for dev / self-hosted gateways)."
    }
  ],
  "tools": [
    {
      "name": "search_papers",
      "description": "Search academic papers by structured filters (title, authors, journal,\nyear, subjects, etc.).\nUse when: \"find Hinton's papers from 2020-2023\", \"Nature papers on\nCRISPR\".\nNot for: natural-language Q&A retrieval (use semantic_search) or\nfull-text snippets (use read_content).\nReturns: list of papers; each entry has unique_id (always present),\ndoc_id (only when full text exists), title, author, abstract,\npublication_venue_name_unified, publication_published_year.",
      "script": "scripts/search_papers.mjs",
      "input_schema": {
        "type": "object",
        "properties": {
          "collection": {
            "type": "string",
            "enum": [
              "papers",
              "authors",
              "sources"
            ],
            "default": "papers",
            "description": "检索的实体集合。papers（默认，论文）/ authors（作者）/ sources（来源期刊）。 各 collection 字段集不同，用 list_catalog（collection=<name>）学习对应 schema。 注意：本工具的便捷字段（authors/journals/year_from/subjects 等）只对 papers 有意义； 查 authors/sources 时改用 filters_advanced + 该 collection 的字段名（如 authors 的 summary_stats.h_index / orcid，sources 的 issn / is_oa）。authors 用 orcid、 sources 用 issn 与论文检索结果关联。",
            "x-en-description": "Entity collection to search. papers (default) / authors / sources. Each collection has its own field schema — call list_catalog(collection=<name>). The convenience fields (authors/journals/year_from/subjects) apply to papers only; for authors/sources use filters_advanced with that collection's field names."
          },
          "query": {
            "type": "string",
            "description": "BM25 全文关键词，匹配标题/摘要/期刊名/关键词字段。留空则纯靠结构化过滤。\n普通关键词是宽松匹配（任一词命中、按相关性排序）。\n\n也支持布尔检索式：全大写 AND / OR / NOT、括号分组、引号短语；优先级\nNOT > AND > OR，相邻词隐式 AND。例如\n  (histopathology OR pathology) AND (\"deep learning\" OR \"machine learning\") AND (prognosis OR survival)\n布尔式里每个检索词都是硬条件、不做放宽——0 命中就是 0。\n- query 只放检索词：「检索式1（预后预测）」这类标签/说明也会变成必须命中的词。\n- 小写 and/or 是普通词；要检索字面量 OR（如比值比）加引号 \"OR\"。\n- 引号需与运算符同时出现才生效：只写 \"spread through air spaces\" 不带运算符时\n  按普通关键词处理；写成 \"spread through air spaces\" AND lung 才是短语精确匹配。

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 9, 2026.

About

Avoid when

Public facts

Install and run

Documentation

Extracted files

SKILL.md

README.md

_meta.json

skill-card.md

manifest.json

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Machine-readable data