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Redis Memory Backend Skill — measured AI agent skill | DecimalAI

a5c-ai/redis-memory-backend

SkillScore82Maintained

Redis backend for conversation state persistence and caching

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-redis-memory-backend

Get this skill — curl · raw file

curl -fsSL https://app.decimal.ai/s/a5c-ai-redis-memory-backend/SKILL.md

Raw file: /s/a5c-ai-redis-memory-backend/SKILL.md ↗ · machine-readable summary at /s/a5c-ai-redis-memory-backend.json ↗

In productionGlobal

how this skill behaves on real traffic · last 30 days

No production data for this skill yet — usage numbers appear here once agents start running it.

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 9d ago

Yes — +36% lift-13% tokens0% turns82% of whole cases pass with skill (18/22)

Test case	Without → With	Effect	Δ tokens	Δ turns
case-04	✗→✓	▲ Improved	-53%	0%
case-05	✗→✓	▲ Improved	7%	0%
case-06	✗→✓	▲ Improved	-35%	0%
case-12	✗→✓	▲ Improved	-3%	0%
case-14	✗→✓	▲ Improved	-37%	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/redis-memory-backend/SKILL.md

View on GitHub ↗

License MITImported 7/31/2026Version v1

Skill contents

SKILL.md

Redis Memory Backend Skill

Capabilities

Configure Redis for conversation state storage
Implement message history persistence
Set up Redis caching for LLM responses
Configure TTL-based memory expiration
Implement Redis Pub/Sub for real-time updates
Design efficient key schemas

Target Processes

conversational-memory-system
chatbot-design-implementation

Implementation Details

Core Components

Message Store: RedisChatMessageHistory
Cache: LLM response caching
State Store: Conversation state persistence
Pub/Sub: Real-time updates

Configuration Options

Redis connection settings
Key prefix configuration
TTL settings
Serialization format
Cluster configuration

Key Schema Patterns

session:{session_id}:messages
cache:llm:{prompt_hash}
state:{user_id}:{key}

Best Practices

Use appropriate data structures
Configure proper TTLs
Implement connection pooling
Monitor memory usage

Dependencies

redis
langchain-community (RedisChatMessageHistory)

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