rag-wrapper
Patterns for wrapping any agent with RAG context from Qdrant. Use to add persistent memory to imported or external agents.
What this skill does
# 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
```markdown
## Retrieved Context
### Source: {metadata.source}
Harvested: {metadata.harvested_at}
Type: {metadata.type}
{document content}
---
```
### Step 3: Prepend to Task
```markdown
{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
```markdown
# 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 in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.