ai mlby dokhacgiakhoa

RAG Engineer

dokhacgiakhoa/rag-engineer

Source dokhacgiakhoa/agent-skills-4-vibe-coding-cli ↗License NOASSERTION

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

NewMaintained

Automatic

AI Engineer pack

Last verified 8/30/2026

What it changes

Passed 22 of 23 test cases with the skill, and 22 of 23 without it.

Without skill96%

With skill96%

0 fixed22 passed either way1 still failing0 broke

Pass-rate lift: 0 pp
Tokens: +3%
Agent turns: 0%
Response time: -9%

Model gemini-3.6-flashMeasured 8/30/2026v122 of 23 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/dokhacgiakhoa-rag-engineer/SKILL.md https://app.decimal.ai/s/dokhacgiakhoa-rag-engineer/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add dokhacgiakhoa-rag-engineer, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/dokhacgiakhoa-rag-engineer/ and pins it in .decimal/skills.lock

decimalai skills add dokhacgiakhoa-rag-engineer

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/dokhacgiakhoa-rag-engineer/SKILL.md https://app.decimal.ai/s/dokhacgiakhoa-rag-engineer/SKILL.md

Raw file: /s/dokhacgiakhoa-rag-engineer/SKILL.md ↗ · machine-readable summary at /s/dokhacgiakhoa-rag-engineer.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

What's inside

The instructions your model reads when the skill loads.

SKILL.md34 linessingle file

RAG Engineer

Role: RAG Systems Architect

I bridge the gap between raw documents and LLM understanding. I know that retrieval quality determines generation quality - garbage in, garbage out. I obsess over chunking boundaries, embedding dimensions, and similarity metrics because they make the difference between helpful and hallucinating.

Capabilities

Vector embeddings and similarity search
Document chunking and preprocessing
Retrieval pipeline design
Semantic search implementation
Context window optimization
Hybrid search (keyword + semantic)

Requirements

LLM fundamentals
Understanding of embeddings
Basic NLP concepts

Patterns

🧠 Knowledge Modules (Fractal Skills)

1. Semantic Chunking

2. Hierarchical Retrieval

3. Hybrid Search

4. ❌ Fixed Chunk Size

5. ❌ Embedding Everything

6. ❌ Ignoring Evaluation

Track record

Benchmark

22 / 23

test cases passed, vs 22 / 23 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/10/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

dokhacgiakhoa / agent-skills-4-vibe-coding-cli

.agent/skills/rag-engineer/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/10/2026Version v1

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Close match

0 pp pass-rate lift

jeffallan/rag-architectDesigns and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.Close match+18 pp pass-rate lift

dokhacgiakhoa/react-native-best-practices

React Native & Expo engineering standards.

+4 pp pass-rate lift

dokhacgiakhoa/react-ui-patternsModern React UI patterns for loading states, error handling, and data fetching. Use when building UI components, handling async data, or managing UI states.+4 pp pass-rate lift

dokhacgiakhoa/receiving-code-reviewUse when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation+32 pp pass-rate lift

dokhacgiakhoa/Red Team Tools and MethodologyThis skill should be used when the user asks to "follow red team methodology", "perform bug bounty hunting", "automate reconnaissance", "hunt for XSS vulnerabilities", "enumerate subdomains", or needs security researcher techniques and tool configurations from top bug bounty hunters.+5 pp pass-rate lift

Ready to supercharge your AI agents?

What's inside

RAG Engineer

Capabilities

Requirements

Patterns

🧠 Knowledge Modules (Fractal Skills)

1. Semantic Chunking

2. Hierarchical Retrieval

3. Hybrid Search

4. ❌ Fixed Chunk Size

5. ❌ Embedding Everything

6. ❌ Ignoring Evaluation

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

Related skills