
Rag Wrapper
- 19 installs
- 2 repo stars
- Updated February 12, 2026
- mindmorass/reflex
Helps with ai & agent building tasks.
About
rag-wrapper is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.
- rag-wrapper
- AI & Agent Building
- AI-coding skill
Rag Wrapper by the numbers
- 19 all-time installs (skills.sh)
- Ranked #10,587 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 19 |
|---|---|
| repo stars | ★ 2 |
| Last updated | February 12, 2026 |
| Repository | mindmorass/reflex ↗ |
What it does
Helps with ai & agent building tasks.
Files
RAG Wrapper Patterns
Patterns for augmenting any agent with Qdrant context retrieval.
Quick Start
To wrap an agent with RAG:
Use rag-proxy agent:
Target: {agent-to-wrap}
Task: {the task}Manual Wrapping Pattern
If you need custom control, follow this pattern:
Step 1: Query Relevant Context
Tool: qdrant-find
Query: {key terms from task}Step 2: Format Context Block
## Retrieved Context
### Source: {metadata.source}
Harvested: {metadata.harvested_at}
Type: {metadata.type}
{document content}
---Step 3: Prepend to Task
{context blocks}
## Task
{original task}
---
Note: Above context is from stored knowledge. Verify if needed.Step 4: Delegate
Tool: Task
Agent: {target-agent}
Prompt: {enriched prompt}Enriched Prompt Template
# Context from Stored Knowledge
The following relevant information was retrieved from project memory:
{{#each contexts}}
## From Qdrant
**Source:** {{metadata.source}}
**Harvested:** {{metadata.harvested_at}}
{{content}}
---
{{/each}}
# Your Task
{{original_task}}
---
**Note:** The context above comes from previously harvested research.
Use it if relevant, but verify currency for time-sensitive information.
The `harvested_at` dates indicate when the content was stored.Selective Wrapping
Not all tasks need RAG. Skip for:
| Task Type | Wrap? | Reason |
|---|---|---|
| Fresh research | No | Need current, not cached data |
| Simple edits | No | Context not needed |
| RAG-aware agents | No | Already query Qdrant |
| Implementation | Yes | Benefit from patterns, decisions |
| Debugging | Yes | Previous solutions may help |
| Architecture | Yes | Decisions and constraints matter |
Agent-Collection Affinity
Map agent types to useful query topics:
| Agent Type | Query Topics |
|---|---|
| frontend-developer | react, design system, components |
| backend-architect | api, architecture, decisions |
| security-auditor | security, authentication, vulnerabilities |
| devops | infrastructure, terraform, deployment |
| tester | testing, coverage, quality |
Storing Results
After the target agent completes:
Tool: qdrant-store
Information: "<valuable findings>"
Metadata:
source: "agent-output"
type: "generated"
harvested_at: "<ISO date>"
tags: "<relevant,keywords>"Error Handling
| Scenario | Action |
|---|---|
| Empty query results | Proceed without context |
| Qdrant unavailable | Fall back to unwrapped delegation |
| Target agent fails | Report error, don't retry with less context |
Related skills
AI & Agent Buildingagents