architecture-agent-creation
Create specialized infrastructure agent definitions for platform/service management (Grafana, Prometheus, Traefik, ERPNext, etc.). Use when the user requests creation of an agent for a specific technology platform or infrastructure component. This skill produces complete agent prompts with integrated research, SOPs, tool references, and handoff protocols following the Linear-First Agentic Workflow framework.
Best use case
architecture-agent-creation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Create specialized infrastructure agent definitions for platform/service management (Grafana, Prometheus, Traefik, ERPNext, etc.). Use when the user requests creation of an agent for a specific technology platform or infrastructure component. This skill produces complete agent prompts with integrated research, SOPs, tool references, and handoff protocols following the Linear-First Agentic Workflow framework.
Teams using architecture-agent-creation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/architecture-agent-creation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How architecture-agent-creation Compares
| Feature / Agent | architecture-agent-creation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Create specialized infrastructure agent definitions for platform/service management (Grafana, Prometheus, Traefik, ERPNext, etc.). Use when the user requests creation of an agent for a specific technology platform or infrastructure component. This skill produces complete agent prompts with integrated research, SOPs, tool references, and handoff protocols following the Linear-First Agentic Workflow framework.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Architecture Agent Creation
## Overview
Create complete, production-ready agent definitions for infrastructure and platform management agents (Grafana, Prometheus, Traefik, ERPNext, n8n, Jupyter, etc.). Each agent definition includes:
1. **Agent prompt file** (`.claude/agents/<agent-name>.md`) with YAML frontmatter
2. **Reference documentation** (3 docs covering best practices, API/CLI reference, troubleshooting)
3. **Integration specifications** (handoff protocols, Linear workflow position, delegation triggers)
4. **Tool requirements** (MCP tools, CLI installation, credentials)
This skill is designed for the **Linear-First Agentic Workflow framework** where Traycer delegates to specialized agents following a 7-Phase TDD workflow.
## When to Use This Skill
Use this skill when:
- User requests "create an agent for [technology]" (e.g., "create a Grafana agent")
- Need to add infrastructure agent to agentic workflow (Prometheus, n8n, Jupyter, Traefik)
- Building agent for specific platform/service (ERPNext, Qdrant, vLLM, OpenWebUI)
- Expanding agent coverage for homelab/infrastructure automation
**Trigger patterns**:
- "Make me an agent for Grafana"
- "Create a Prometheus monitoring agent"
- "I need an agent to manage Traefik reverse proxy"
- "Build an ERPNext integration agent"
**NOT for**:
- Workflow coordination agents (Planning, Action, QA, Tracking) - use `agent-builder` skill instead
- General-purpose coding tasks
- One-off automation scripts
## Core Capabilities
### 1. Agent Definition Research
Research the target technology platform to understand:
- **Purpose & architecture** - What the technology does, how it works
- **Tool ecosystem** - CLI tools, APIs, configuration methods
- **Integration patterns** - How to connect with other services
- **Common workflows** - Typical operational tasks and procedures
- **Best practices** - Security, performance, reliability patterns
- **Real-world examples** - Working commands with expected output
**Tools Used**: WebFetch, WebSearch, official documentation, community resources
**Output**: Comprehensive research brief covering all aspects needed for agent creation
### 2. Agent Prompt Generation
Create complete agent prompt with sections:
- **Agent Identity** - Name, domain, primary responsibility, delegation triggers
- **Core Capabilities** - 3-4 major capability categories with tools and patterns
- **Technology Stack** - Version numbers, tools, dependencies, documentation sources
- **Standard Operating Procedures** - Step-by-step workflows for common tasks
- **Best Practices** - DO/DON'T lists for proper usage
- **Workflow Position** - Where agent fits in 7-Phase TDD workflow
- **Integration Points** - Handoff sources and targets
- **Security Considerations** - Secrets management, access control, vulnerabilities
- **Handoff Protocol** - Incoming/outgoing handoff formats
- **Tool Inventory** - MCP tools, CLI tools, file operations
- **Error Handling** - Common failures, retry strategy, escalation criteria
- **Quality Checklist** - Pre-completion verification items
- **Example Workflows** - 2-3 complete scenarios with commands and output
- **Tool Installation** - Installation commands for all required tools
**Tools Used**: Write tool to create agent prompt file
**Output**: Complete agent prompt file at `.claude/agents/<agent-name>.md`
### 3. Reference Documentation Creation
Generate 3 reference documents in `docs/agents/<agent-name>/ref-docs/`:
**1. Best Practices Guide** (600-800 words):
- Configuration management patterns
- Performance optimization techniques
- Reliability and HA patterns
- Security hardening steps
- Monitoring and observability
- Common pitfalls to avoid
**2. API/CLI Reference** (600-800 words):
- Essential CLI commands with examples
- API endpoints with curl commands and expected responses
- Configuration file formats with YAML/JSON examples
- Data schemas for key resources
- Authentication methods
**3. Troubleshooting Guide** (600-800 words):
- Diagnostic procedures with commands
- Common errors with symptoms and resolutions
- Performance issue diagnosis
- Integration problem solving
- Health check procedures
**Tools Used**: Write tool to create reference docs
**Output**: 3 reference markdown files with concrete, actionable examples
### 4. Integration Specification
Define how agent integrates into agentic workflow:
**Delegation Triggers**: Keywords/patterns that trigger agent from Traycer
**Handoff Sources**: Which agents delegate TO this agent
**Handoff Targets**: Which agents this agent delegates TO
**Linear Workflow Position**: Which phases (Research/Spec/QA/Action/Tracking) agent operates in
**Linear Integration**: MCP tool usage with project filtering examples
**Tools Used**: None (integrated into agent prompt)
**Output**: Integration notes section in agent prompt
## Standard Operating Procedures
### SOP-1: Create Infrastructure Agent from Scratch
**Trigger**: User requests "create an agent for [technology]"
**Steps**:
1. **Gather Requirements**
```bash
# Confirm technology details with user
# - Technology name and version
# - Primary use case (what will agent manage?)
# - Integration points (which other agents will it work with?)
```
2. **Research Phase** (15-20 minutes)
- Use WebFetch/WebSearch to gather:
- Official documentation (architecture, API reference, CLI tools)
- Best practices from production deployments
- Common workflows and operational patterns
- Security considerations and hardening steps
- Real command examples with expected output
- Create research brief in `docs/.scratch/<issue-id>/research-brief.md`
3. **Generate Agent Prompt** (10-15 minutes)
- Use template from `references/agent-prompt-template.md`
- Fill ALL sections with researched content (no placeholders)
- Include concrete examples, working commands, expected output
- Specify exact version numbers for tools
- Define retry strategy with timings (2s, 4s, 8s backoff)
- Add Linear MCP integration with project filtering code
- Write to `.claude/agents/<agent-name>.md`
4. **Create Reference Documentation** (15-20 minutes)
- Generate 3 reference docs using templates:
- `<agent-name>-best-practices.md` - Configuration, performance, security patterns
- `<agent-name>-api-reference.md` - CLI commands, API endpoints, schemas
- `<agent-name>-troubleshooting.md` - Diagnostics, common errors, resolutions
- Write to `docs/agents/<agent-name>/ref-docs/`
5. **Add Integration Specifications** (5 minutes)
- Define delegation triggers (keywords that invoke agent)
- Specify handoff sources/targets (which agents communicate)
- Document Linear workflow position (which TDD phases)
- List tool requirements (MCP servers, CLI tools, credentials)
6. **Validation Check**
- [ ] Agent prompt 1,200-1,500 words with ALL sections filled
- [ ] No placeholders like `[command]` or `[description]`
- [ ] Each SOP has step-by-step commands (not abstract descriptions)
- [ ] API examples include full curl commands and expected responses
- [ ] Security section covers secrets, access control, vulnerabilities
- [ ] Retry strategy specifies exact backoff timings
- [ ] Linear MCP integration includes project filtering code
- [ ] 7-Phase workflow position is clear
- [ ] Reference docs have concrete examples (not theoretical)
- [ ] Tool installation has actual commands for all tools
**Output**: Complete agent definition ready for use
**Handoff**: Report completion to Traycer with agent file paths
### SOP-2: Update Existing Infrastructure Agent
**Trigger**: User requests "update [agent] to add [capability]"
**Steps**:
1. **Read Existing Agent Prompt**
```bash
# Read current agent file
Read .claude/agents/<agent-name>.md
```
2. **Research New Capability**
- Use WebFetch/WebSearch to research new feature
- Gather commands, API endpoints, examples
- Identify integration changes needed
3. **Update Agent Prompt**
- Add new capability to "Core Capabilities" section
- Update relevant SOP or add new SOP
- Add to "Tool Inventory" if new tools needed
- Update "Example Workflows" if applicable
4. **Update Reference Docs (if needed)**
- Add new API endpoints to API reference
- Add new best practices
- Add troubleshooting for new errors
5. **Validation Check**
- [ ] New capability fully documented with examples
- [ ] SOPs updated with step-by-step commands
- [ ] No conflicts with existing capabilities
- [ ] All sections still have concrete examples
**Output**: Updated agent definition
**Handoff**: Report changes to Traycer
## Best Practices
**DO**:
- ✅ Research official documentation FIRST before writing agent prompt
- ✅ Include actual commands with expected output (not placeholders)
- ✅ Specify exact version numbers (Grafana 11.x, Prometheus 2.48+)
- ✅ Define retry strategy with specific timings (2s, 4s, 8s backoff)
- ✅ Include Linear MCP project filtering code examples
- ✅ Write 2-3 complete example workflows with full command sequences
- ✅ Provide tool installation commands for all dependencies
- ✅ Cover 4+ common errors with symptoms and resolutions
- ✅ Reference official docs as sources
**DO NOT**:
- ❌ Leave placeholder text like `[command]` or `[description]`
- ❌ Write generic descriptions without concrete examples
- ❌ Skip security considerations section
- ❌ Omit retry strategy or escalation criteria
- ❌ Forget Linear MCP project filtering (causes cross-contamination)
- ❌ Create agent without researching current best practices
- ❌ Use outdated version numbers (check latest stable releases)
- ❌ Copy examples without verifying they work
## Workflow Position in 7-Phase TDD
**This skill operates in**:
- **Phase 1 (Research)**: When Research Agent creates infrastructure agent definition
**Receives handoffs from**:
- **Traycer**: User request to create new infrastructure agent
**Delegates to**:
- None (produces deliverable directly)
**Typical flow**:
1. User → Traycer: "Create a Grafana agent"
2. Traycer → Planning Agent: "Use architecture-agent-creation skill"
3. Planning Agent (this skill):
- Researches Grafana using WebFetch/WebSearch
- Generates complete agent prompt with all sections
- Creates 3 reference docs with examples
- Validates completeness
4. Planning Agent → Traycer: "Grafana agent created at `.claude/agents/grafana-agent.md`"
## Resources
This skill includes reference materials demonstrating the expected output format:
### references/
**agent-prompt-template.md**: Complete template showing all required sections for an infrastructure agent prompt. Based on the enriched Grafana agent prompt with all corrections baked in. Use this as the blueprint for all new agent definitions.
**research-checklist.md**: Systematic checklist of research areas to cover when creating a new agent. Ensures no critical information is missed during the research phase.
**agent-definition-schema.md**: Formal schema defining all sections, required content types, word counts, and validation criteria for complete agent definitions.
### assets/
*No assets required for this skill - output is markdown text files.*
### scripts/
*No scripts required - skill uses built-in tools (Read, Write, WebFetch, WebSearch).*
## Quality Standards
### Agent Prompt Quality
**Length**: 1,200-1,500 words minimum
**Required Sections** (all must be present):
- Agent Identity (name, domain, responsibility, triggers)
- Core Capabilities (3-4 categories with tools and patterns)
- Technology Stack (versions, tools, dependencies, docs)
- Standard Operating Procedures (3+ SOPs with commands)
- Best Practices (DO/DON'T lists)
- Workflow Position (7-Phase TDD integration)
- Integration Points (handoff sources/targets)
- Security Considerations (secrets, access control, vulnerabilities)
- Handoff Protocol (incoming/outgoing formats)
- Tool Inventory (MCP tools, CLI tools, file ops)
- Error Handling (common failures, retry strategy, escalation)
- Quality Checklist (pre-completion verification)
- Example Workflows (2-3 complete scenarios)
- Tool Installation (commands for all tools)
**Content Standards**:
- No placeholders - all sections filled with concrete content
- Real commands with expected output (not generic descriptions)
- Specific version numbers (not "latest")
- Actual error messages with resolutions
- Working code examples (curl commands, YAML configs)
### Reference Documentation Quality
**Length**: 600-800 words per document (3 docs total: 1,800-2,400 words)
**Content Standards**:
- Concrete examples (not theoretical concepts)
- Real commands that can be copied and executed
- Expected output shown for all commands
- Error messages with exact text and resolutions
- Version-specific details where relevant
## Example Output Structure
```markdown
.claude/agents/grafana-agent.md # Main agent prompt (1,200-1,500 words)
docs/agents/grafana-agent/
└── ref-docs/
├── grafana-best-practices.md # Best practices (600-800 words)
├── grafana-api-reference.md # API/CLI reference (600-800 words)
└── grafana-troubleshooting.md # Troubleshooting (600-800 words)
```
## Integration with Agentic Workflow
**Trigger Keywords** (from Traycer):
- "create an agent for [technology]"
- "make me a [technology] agent"
- "build an agent to manage [technology]"
- "need an agent for [platform]"
**Linear Workflow**:
- Used by Planning Agent when infrastructure agent needed
- Not typically invoked by other specialized agents
- Creates deliverables (agent files) but doesn't update Linear directly
**Handoff Protocol**:
- Receives: User request from Traycer (conversational)
- Produces: Agent prompt + reference docs (file artifacts)
- Reports: Completion to Traycer with file paths
## Common Pitfalls
**Pitfall 1: Placeholder Content**
- **Symptom**: Agent prompt contains `[command]`, `[description]`, `[TODO]`
- **Impact**: Agent is unusable - lacks actionable guidance
- **Solution**: Research thoroughly BEFORE writing, fill ALL sections with concrete examples
**Pitfall 2: Generic Descriptions**
- **Symptom**: SOPs say "run appropriate commands" instead of showing actual commands
- **Impact**: Agent doesn't know what to do
- **Solution**: Include full curl commands, config files, expected output
**Pitfall 3: Outdated Version Info**
- **Symptom**: References Grafana 9.x when current is 11.x
- **Impact**: Syntax errors, deprecated APIs, broken examples
- **Solution**: Check official docs for latest stable version FIRST
**Pitfall 4: Missing Linear MCP Filtering**
- **Symptom**: Agent uses `list_issues()` without project filter
- **Impact**: Sees ALL issues from ALL teams (cross-contamination)
- **Solution**: Always include project filtering code example in agent prompt
**Pitfall 5: Vague Retry Strategy**
- **Symptom**: "Retry on network errors" without timing details
- **Impact**: Agent retries indefinitely or escalates prematurely
- **Solution**: Specify exact backoff timings (2s, 4s, 8s) and escalation triggers
**Pitfall 6: No Security Section**
- **Symptom**: Agent prompt lacks secrets management guidance
- **Impact**: Hardcoded secrets in configs, security vulnerabilities
- **Solution**: Always include secrets, access control, and vulnerability sections
## Validation Checklist
Before marking agent complete, verify:
- [ ] Agent prompt is 1,200-1,500 words with ALL sections filled
- [ ] Each of 3 reference docs is 600-800 words with concrete examples
- [ ] SOPs have step-by-step commands (not placeholders like `[command]`)
- [ ] API examples include full curl commands and expected JSON responses
- [ ] Security section covers secrets, access control, common vulnerabilities
- [ ] Retry strategy specifies exact backoff timings and escalation triggers
- [ ] Linear integration includes MCP tool names and project filtering code
- [ ] 7-Phase workflow position is clear with specific handoff agents
- [ ] Quality checklist includes security scan requirement
- [ ] Example workflows show complete command sequences with output
- [ ] Tool installation section has actual commands for all tools
- [ ] Error handling covers 4+ common failures with resolutions
- [ ] Technology stack specifies exact version numbers (not "latest")
- [ ] Reference docs saved in correct directory structure
- [ ] All file paths use repo-relative paths (no user-specific absolute paths)
## Technical Notes
**Tool Requirements**:
- **WebFetch** - Required for fetching official documentation
- **WebSearch** - Required for finding community best practices
- **Read** - For reading existing agent files during updates
- **Write** - For creating agent prompt and reference docs
- **Glob** - For finding existing agent files
**No MCP servers required** - This skill uses built-in Claude Code tools only.
**Output Format**: All files are GitHub-flavored Markdown with YAML frontmatter (for agent prompts).
**File Naming Convention**:
- Agent prompt: `<technology>-agent.md` (lowercase with hyphens)
- Reference docs: `<technology>-<topic>.md` (e.g., `grafana-best-practices.md`)
**Target Audience**: The agent prompts are written for Claude instances (not humans), so use imperative/infinitive form ("Create dashboard", "Test connection") rather than second person ("You should create").Related Skills
MCP Architecture Expert
Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices
data-model-creation
Professional rules for AI-driven data modeling and creation. Use this skill when users need to create and manage MySQL databases, design data models using Mermaid ER diagrams, and implement database schemas.
architecture-paradigm-pipeline
Consult this skill when designing data pipelines or transformation workflows. Use when data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial. Do not use when selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow is not sequential or predictable. DO NOT use when: complex branching/merging logic dominates.
architecture-advisor
Helps solo developers with AI agents choose optimal architecture (monolithic/microservices/hybrid)
agent-native-architecture
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
agent-creation
Standards-compliant agent definitions with templates. Trigger: When creating agent definitions, setting up project agents, or documenting workflows.
agent-architecture
Use when designing or implementing AI agent systems. Covers tool-using agents with mandatory guardrails, SSE streaming (FastAPI → Next.js via Vercel AI SDK v6), LangGraph stateful multi-agent graphs, episodic memory via pgvector, MCP overview, and production failure modes with anti-pattern/fix code pairs.
content-creation-flow
Step-by-step content creation workflow for documentation and training materials. Includes MCP validation, discovery, content design, Jira planning, and draft generation with priority-based execution. Use when starting the content creation flow, creating documentation or training materials, or when the user asks to follow the content creation workflow.
Automate YouTube Top-Ten Video Creation with OpenAI and Safe Image Search
Integrates OpenAI API for content generation, Bing Image Search API for safe image retrieval, and Pexels API for video footage. Handles authentication via Bearer token, enforces safe search, formats ChatGPT responses into a top-ten list, and includes error handling for API failures.
ai-podcast-creation
Create AI-powered podcasts with text-to-speech, music, and audio editing. Tools: Kokoro TTS, DIA TTS, Chatterbox, AI music generation, media merger. Capabilities: multi-voice conversations, background music, intro/outro, full episodes. Use for: podcast production, audiobooks, voice content, audio newsletters. Triggers: podcast, ai podcast, text to speech podcast, audio content, voice over, ai audiobook, multi voice, conversation ai, notebooklm alternative, audio generation, podcast automation, ai narrator, voice content, audio newsletter, podcast maker
u07820-attention-management-architecture-for-personal-finance-management
Build and operate the "Attention Management Architecture for personal finance management" capability for personal finance management. Use when this exact capability is required by autonomous or human-guided missions.
MCP Server Architecture
This skill should be used when the user asks to "create an MCP server", "set up MCP server", "build ChatGPT app backend", "MCP transport type", "configure MCP endpoint", "server setup for Apps SDK", or needs guidance on MCP server architecture, transport protocols, or SDK setup for the OpenAI Apps SDK.