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a5c-ai/rag-query-transformation

SkillScore95Maintained

Query expansion, HyDE, and multi-query generation for improved retrieval

by a5c-ai

Links this skill to your agents and keeps it current with the author’s updates.

Automatic

AI Engineer pack

decimalai skills pull a5c-ai-rag-query-transformation

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/a5c-ai-rag-query-transformation/SKILL.md

Raw file: /s/a5c-ai-rag-query-transformation/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-rag-query-transformation.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

Runs

all versions · 30d

Used when offered

used / offered · all versions

Anonymized aggregate across every org using this skill — proof from real production use, not a demo.

Does this skill help? · Benchmark: with vs. withoutsign in to run this on your agents

22 test cases from the publisher’s eval.yaml · each run twice on gemini-3.6-flash — with the skill injected, then without · last run 8d ago

Yes — +14% lift+4% tokens0% turns95% of whole cases pass with skill (21/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-01	✗→✓	▲ Improved	-7%	0%
case-11	✗→✓	▲ Improved	-43%	0%
case-15	✗→✓	▲ Improved	-36%	0%
case-02	✓→✓	＝ Same ✓	23%	0%
case-03	✓→✓	＝ Same ✓	14%	0%

Showing 5 of 22 cases

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 7/31/2026

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

a5c-ai / babysitter

library/specializations/ai-agents-conversational/skills/rag-query-transformation/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1

Skill contents

SKILL.md

RAG Query Transformation Skill

Capabilities

Implement query expansion techniques
Configure Hypothetical Document Embeddings (HyDE)
Set up multi-query generation
Design query decomposition strategies
Implement step-back prompting
Configure query routing for specialized indices

Target Processes

advanced-rag-patterns
knowledge-base-qa

Implementation Details

Transformation Techniques

Multi-Query Generation: Generate query variations
HyDE: Generate hypothetical answer, embed that
Query Decomposition: Break complex queries into sub-queries
Step-Back Prompting: Generate higher-level queries
Query Expansion: Add synonyms and related terms

Configuration Options

Number of query variations
LLM for query generation
Decomposition depth
Query routing rules
Result fusion strategy

Best Practices

Match technique to query complexity
Test with representative queries
Monitor retrieval quality changes
Balance latency vs quality tradeoffs

Dependencies

langchain
LLM provider

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Other measured skills in the registry, with their headline benchmark lift.