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denser-retriever更密集的猎犬

Agent Skill

denser-retriever 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

685

周安装

28

GitHub Stars

6

下载量

220
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:denser-retriever(更密集的猎犬)
来源仓库:https://github.com/denser-org/claude-skills
仓库路径:skills/denser-retriever
安装命令:
npx skills add https://github.com/denser-org/claude-skills --skill denser-retriever
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/denser-org/claude-skills --skill denser-retriever

简介

denser-retriever 通过 REST API 管理知识库并执行语义搜索,支持文档检索与向量索引。

  • 适用于企业内部知识问答、FAQ 匹配或技术文档智能查找等场景。
  • 所有操作通过 curl 命令完成,需配置 DENSER_API_KEY 环境变量获取认证。
  • 使用前请确认 API 配额与计费策略,避免超出免费额度产生额外费用。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Denser Retriever API

Manage knowledge bases and perform semantic search over documents via the Denser Retriever REST API. All operations use curl commands.

Setup

API Key: Read from DENSER_API_KEY environment variable. If not set, ask the user for their API key.

Base URL: https://retriever.denser.ai/api/open/v1

To check if the key is available:

echo $DENSER_API_KEY

If empty, ask the user: "Please provide your Denser Retriever API key (from your organization settings)."

Quick Reference

OperationMethodEndpoint
Get usageGET/v1/getUsage
Get balanceGET/v1/getBalance
Create KBPOST/v1/createKnowledgeBase
List KBsGET/v1/listKnowledgeBases
Update KBPOST/v1/updateKnowledgeBase
Delete KBPOST/v1/deleteKnowledgeBase
Upload file (presign)POST/v1/presignUploadUrl
Import filePOST/v1/importFile
Import textPOST/v1/importTextContent
List documentsGET/v1/listDocuments
Delete documentPOST/v1/deleteDocument
Check doc statusGET/v1/getDocumentStatus
Search/queryPOST/v1/query

For full request/response schemas, read references/api_reference.md.

Common Workflows

1. Build a Knowledge Base from Files

This is a multi-step process: create KB, upload each file, wait for processing, then search.

Step 1: Create a knowledge base

curl -s -X POST "https://retriever.denser.ai/api/open/v1/createKnowledgeBase" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name": "My KB", "description": "Optional description"}' | python3 -m json.tool

Save the returned id for subsequent operations.

Step 2: For each file, do a 3-step upload

a) Get a presigned upload URL:

curl -s -X POST "https://retriever.denser.ai/api/open/v1/presignUploadUrl" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"knowledgeBaseId": "KB_ID", "fileName": "document.pdf", "size": FILE_SIZE_BYTES}' | python3 -m json.tool

b) Upload the file to the presigned URL (raw bytes, PUT request):

curl -s -X PUT "PRESIGNED_UPLOAD_URL" --data-binary @/path/to/document.pdf

c) Trigger import processing:

curl -s -X POST "https://retriever.denser.ai/api/open/v1/importFile" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"fileId": "FILE_ID"}' | python3 -m json.tool

Step 3: Poll until processed

curl -s -X GET "https://retriever.denser.ai/api/open/v1/getDocumentStatus?documentId=DOC_ID" \
  -H "x-api-key: $DENSER_API_KEY" | python3 -m json.tool

Repeat every 2-3 seconds until status is "processed". If "failed" or "timeout", report the error.

Step 4: Search

curl -s -X POST "https://retriever.denser.ai/api/open/v1/query" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "your search query", "knowledgeBaseIds": ["KB_ID"], "limit": 10}' | python3 -m json.tool

2. Import Text Content Directly

For plain text that doesn't need file upload:

curl -s -X POST "https://retriever.denser.ai/api/open/v1/importTextContent" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"knowledgeBaseId": "KB_ID", "title": "My Document", "content": "Full text content here..."}' | python3 -m json.tool

3. Search Across All Knowledge Bases

Omit knowledgeBaseIds to search everything:

curl -s -X POST "https://retriever.denser.ai/api/open/v1/query" \
  -H "x-api-key: $DENSER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "your search query", "limit": 10}' | python3 -m json.tool

Automation Patterns

Batch Upload Multiple Files

When the user provides a directory of files, loop through them:

KB_ID="your-kb-id"
for file in /path/to/files/*; do
  FILE_NAME=$(basename "$file")
  FILE_SIZE=$(stat -f%z "$file" 2>/dev/null || stat -c%s "$file" 2>/dev/null)

  # Get presigned URL
  PRESIGN=$(curl -s -X POST "https://retriever.denser.ai/api/open/v1/presignUploadUrl" \
    -H "x-api-key: $DENSER_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"knowledgeBaseId\": \"$KB_ID\", \"fileName\": \"$FILE_NAME\", \"size\": $FILE_SIZE}")

  UPLOAD_URL=$(echo "$PRESIGN" | python3 -c "import sys,json; print(json.load(sys.stdin)['data']['uploadUrl'])")
  FILE_ID=$(echo "$PRESIGN" | python3 -c "import sys,json; print(json.load(sys.stdin)['data']['fileId'])")

  # Upload file
  curl -s -X PUT "$UPLOAD_URL" --data-binary @"$file"

  # Trigger import
  curl -s -X POST "https://retriever.denser.ai/api/open/v1/importFile" \
    -H "x-api-key: $DENSER_API_KEY" \
    -H "Content-Type: application/json" \
    -d "{\"fileId\": \"$FILE_ID\"}"

  echo "Uploaded: $FILE_NAME (fileId: $FILE_ID)"
done

Poll Document Status Until Ready

DOC_ID="your-document-id"
while true; do
  STATUS=$(curl -s -X GET "https://retriever.denser.ai/api/open/v1/getDocumentStatus?documentId=$DOC_ID" \
    -H "x-api-key: $DENSER_API_KEY" | python3 -c "import sys,json; print(json.load(sys.stdin)['data']['status'])")
  echo "Status: $STATUS"
  if [ "$STATUS" = "processed" ] || [ "$STATUS" = "failed" ] || [ "$STATUS" = "timeout" ]; then
    break
  fi
  sleep 3
done

Response Format

All responses follow this structure:

Success: {"success": true, "data": {...}}

Error: {"success": false, "message": "Error details", "errorCode": "ERROR_CODE"}

Common error codes: STORAGE_LIMIT_EXCEEDED, KNOWLEDGE_BASE_LIMIT_EXCEEDED, INSUFFICIENT_CREDITS, INPUT_VALIDATION_FAILED

Supported File Types

PDF, DOCX, PPTX, XLS, XLSX, HTML, TXT, CSV, XML, Markdown. Max file size: 512MB.

Important Notes

  • Each search query costs 1 credit. Check balance with getBalance before bulk searches.
  • Document processing is async — always poll getDocumentStatus before searching.
  • The presignUploadUrl -> PUT upload -> importFile flow is required for file uploads.
  • Search results include score, content, title, document_id, and metadata.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.87%
按下载量换算77

Claude

29.41%
按下载量换算65

Cursor

19.97%
按下载量换算44

Gemini CLI

11.02%
按下载量换算24

安全审计

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可疑

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敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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