Tencent SkillHub · AI

RAG

Build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.

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High Signal

Build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.

⬇ 0 downloads ★ 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.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, architecture.md, evaluation.md, implementation.md, security.md

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.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ivangdavila
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 6 sections Open source page

When to Use

User wants to implement, improve, or troubleshoot Retrieval-Augmented Generation systems.

Quick Reference

TopicFilePipeline components & architecturearchitecture.mdImplementation patterns & codeimplementation.mdEvaluation metrics & debuggingevaluation.mdSecurity & compliancesecurity.md

Core Capabilities

Architecture design — Select embedding models, vector DBs, and chunking strategies based on requirements Implementation — Write ingestion pipelines, query handlers, and update logic Retrieval optimization — Tune top-k, reranking, hybrid search parameters Evaluation — Build test datasets, measure recall/precision, diagnose failures Production ops — Monitor quality drift, set up alerts, debug degradation Security — PII detection, access control, compliance requirements

Decision Checklist

Before recommending architecture, ask: What document types and volume? Latency requirements (real-time chat vs batch)? Update frequency (how often do docs change)? Access control needs (who can see what)? Compliance constraints (GDPR, HIPAA, SOC2)? Budget (managed vs self-hosted, embedding costs)?

Critical Rules

Never skip access control — Filter at retrieval time, not after Always overlap chunks — 10-20% prevents context loss at boundaries Evaluate before optimizing — Build eval dataset first, then tune Same embedding model — Query and documents must use identical model Monitor similarity scores — Dropping averages signal drift or issues Plan for deletion — GDPR erasure requires re-embedding capability

Common Failure Patterns

SymptomLikely CauseFixWrong docs retrievedQuery too vague, poor chunksQuery expansion, smaller chunksRelevant doc missedNot indexed, low similarityCheck ingestion, hybrid searchHallucinated answersContext too shortIncrease top-k, better rerankingSlow responsesLarge chunks, no cachingOptimize chunk size, cache embeddingsInconsistent resultsNon-deterministic rerankingSet seeds, use stable sorting

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

5 Docs

SKILL.md Primary doc
architecture.md Docs
evaluation.md Docs
implementation.md Docs
security.md Docs