Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Ragie.ai-RAG
Execute Retrieval-Augmented Generation (RAG) using Ragie.ai. Use this skill whenever the user wants to: - Search their knowledge base - Ask questions about u...
skill openclawclawhub Free
Execute Retrieval-Augmented Generation (RAG) using Ragie.ai. Use this skill whenever the user wants to: - Search their knowledge base - Ask questions about u...
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/ingest.py, scripts/manage.py, scripts/retrieve.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- Hatim-BE
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Ragie.ai RAG Skill (OpenClaw Optimized)
This skill enables grounded question answering using Ragie.ai as a RAG backend. Ragie handles: Document chunking Embedding Vector indexing Retrieval Optional reranking The agent handles: Deciding when to ingest Triggering retrieval Constructing grounded prompts Producing final answers
Core Principles
Never answer without retrieval. Never hallucinate information not present in retrieved chunks. Always cite the document_name when referencing specific facts. If retrieval returns zero relevant chunks, explicitly say: "I don't have that information in the current knowledge base." Do not expose API keys or raw API payloads in final answers.
Case A โ User Provides a File or URL
IF the user provides: A file A document path A PDF/URL to ingest THEN: Execute ingestion: python `skills/scripts/ingest.py` --file <path> --name "<document_name>" OR python `skills/scripts/ingest.py` --url "<url>" --name "<document_name>" Capture returned document_id. Poll document status: python `skills/scripts/manage.py` status --id <document_id> Repeat until status == ready. Proceed to Retrieval (Case C).
List documents
python `skills/scripts/manage.py` list
Check document status
python `skills/scripts/manage.py` status --id <document_id>
Delete a document
python `skills/scripts/manage.py` delete --id <document_id> Return structured results to the user.
Case C โ Retrieval (Grounded Question Answering)
Execute: python `skills/scripts/retrieve.py` \ --query "<user_question>" \ --top-k 6 \ --rerank Optional flags: --partition <name> --filter '{"key":"value"}'
Retrieval Output Format
Expected output: [ { "text": "...", "score": 0.87, "document_name": "Policy Handbook", "document_id": "doc_abc123" } ]
Grounded Prompt Construction
After retrieval: Extract all chunk text. Concatenate with separators. Construct this prompt: SYSTEM: You are a helpful assistant. Answer using ONLY the context provided below. If the context does not contain the answer, say: "I don't have that information in the current knowledge base." CONTEXT: [chunk 1 text] --- [chunk 2 text] --- ... USER QUESTION: {original user question} Generate final answer. Cite document_name when referencing information.
Output Contract
The final response MUST: Be grounded only in retrieved chunks Cite document_name for factual claims Avoid hallucinations Avoid mentioning internal execution steps Avoid exposing API keys or raw responses Clearly state when information is missing If no chunks are returned: I don't have that information in the current knowledge base.
API Reference
Base URL: https://api.ragie.ai OperationMethodEndpointIngest filePOST/documentsIngest URLPOST/documents/urlRetrieve chunksPOST/retrievalsList documentsGET/documentsGet documentGET/documents/{id}Delete documentDELETE/documents/{id}
Error Handling
HTTP CodeMeaningAction404Document not foundVerify document_id422Invalid payloadValidate request schema429Rate limitedRetry with backoff5xxServer errorRetry or check Ragie status If ingestion fails: Report failure clearly. Do not proceed to retrieval. If retrieval fails: Retry once. If still failing, inform user.
Decision Rules Summary
If user uploads content โ ingest โ wait until ready โ retrieve. If user asks question โ retrieve immediately. If zero chunks โ state knowledge gap. Always use reranking unless explicitly disabled. Never answer without retrieval.
Advanced Usage
Use metadata filter to narrow retrieval scope. Use partitions to separate tenant data. Use recency_bias only when time relevance matters. Adjust top_k depending on query complexity.
Security
API keys must be loaded from environment variables. .env must not be committed. Do not log sensitive headers.
Summary
This skill provides: Deterministic ingestion Deterministic retrieval Strict grounded answering Complete Ragie lifecycle management Safe and hallucination-resistant RAG execution End of Skill.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts1 Docs
- SKILL.md
- scripts/ingest.py
- scripts/manage.py
- scripts/retrieve.py