ai-agent-engineer
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: ai-agent-engineer
description: Specialist skill for AI agent engineering in the IdeaFlow codebase. Use when (1) improving agent…
category: ai
runtime: no special runtime
---
# ai-agent-engineer output preview
## PART A: Task fit
- Use case: Specialist skill for AI agent engineering in the IdeaFlow codebase. Use when (1) improving agent configurations (CMZ.json, oh-my-opencode.json), (2) creating or modifying agent skills, (3) enhancing agent orchestration patterns, (4) implementing self-* capabilities (self-heal, self-learn, self-evolve), (5) working with OpenCode CLI, OhMyOpenCode, or Superpowers frameworks, or (6) labeled issues with 'ai-agent-engineer'. Provides domain-specific knowledge for multi-agent systems, delegation patterns, and agent best practices. Use when this capability is needed..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Domain Scope / Agent Architecture / Primary Orchestrator: CMZ” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Specialist skill for AI agent engineering in the IdeaFlow codebase. Use when (1) improving agent configurations (CMZ.json, oh-my-opencode.json), (2) creating or modifying agent skills, (3) enhancing agent orchestration patterns, (4) implementing self-* capabilities (self-heal, self-learn, self-evolve), (5) working with OpenCode CLI, OhMyOpenCode, or Superpowers frameworks, or (6) labeled issues with 'ai-agent-engineer'. Provides domain-specific knowledge for multi-agent systems, delegation patterns, and agent best practices. Use when this capability is needed.”.
- **02** When the source has headings, the agent prioritizes “Domain Scope / Agent Architecture / Primary Orchestrator: CMZ” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Validate with a small run
Start with a small task and check whether the result follows “Domain Scope / Agent Architecture / Primary Orchestrator: CMZ”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: ai-agent-engineer
description: Specialist skill for AI agent engineering in the IdeaFlow codebase. Use when (1) improving agent…
category: ai
source: tomevault-io/skills-registry
---
# ai-agent-engineer
## When to use
- Specialist skill for AI agent engineering in the IdeaFlow codebase. Use when (1) improving agent configurations (CMZ.j…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Domain Scope / Agent Architecture / Primary Orchestrator: CMZ” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "ai-agent-engineer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Domain Scope / Agent Architecture / Primary Orchestrator: CMZ
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}AI Agent Engineer
Expert guidance for engineering and improving AI agents in the IdeaFlow multi-agent system.
Domain Scope
This skill covers the ai-agent-engineer domain within IdeaFlow:
| Area | Files | Purpose |
| ------------------- | ------------------------------------------------------------ | --------------------------------------- | ----------------------------- | --------------------- |
| Agent Configuration | .opencode/agents/CMZ.json, .opencode/oh-my-opencode.json | Agent definitions, models, capabilities |
| YH | | Skills Library | .opencode/skills/*/SKILL.md | 33 specialized skills |
| Agent Guidelines | docs/agent-guidelines.md | 10 core principles, workflows |
| System Integration | opencode.json, AGENTS.md | CLI config, documentation |
Agent Architecture
Primary Orchestrator: CMZ
CMZ (Cognitive Meta-Z) is the main orchestrating agent with three core capabilities:
- Self-Heal: Detect errors, diagnose root cause, implement recovery
- Self-Learn: Integrate feedback, analyze outcomes, build knowledge
- Self-Evolve: Expand capabilities, optimize performance, meta-improve
Specialized Agents (OhMyOpenCode)
| Agent | Model | Purpose |
|---|---|---|
| Sisyphus | minimax-m2.5-free | Main orchestrator, relentless execution |
| Hephaestus | glm-4.7-free | Autonomous deep worker |
| Oracle | minimax-m2.5-free | Architecture, debugging, reasoning |
| Librarian | glm-4.7-free | Documentation, exploration |
| Explore | glm-4.7-free | Fast codebase search |
Categories
| Category | Model | Use Case |
|---|---|---|
| visual-engineering | glm-4.7-free | UI/Frontend work |
| ultrabrain | minimax-m2.5-free | Complex logic, architecture |
| quick | minimax-m2.1-free | Fast, simple tasks |
| deep | minimax-m2.5-free | Thorough analysis |
Delegation Patterns
When to Delegate
Task Type → Delegate To
─────────────────────────────────────
Codebase exploration → explore (background)
Documentation lookup → librarian (background)
Complex reasoning → oracle (blocking)
UI/Frontend work → visual-engineering category
Quick fixes → quick categoryDelegation Commands
# Background exploration (parallel)
task(subagent_type="explore", run_in_background=true, prompt="...")
# Blocking consultation
task(subagent_type="oracle", run_in_background=false, prompt="...")
# Category delegation
task(category="visual-engineering", load_skills=["frontend-ui-ux"])Configuration Management
Key Files
| File | Purpose |
|---|---|
opencode.json |
CLI config, model selection, MCP servers |
.opencode/oh-my-opencode.json |
Agent definitions, categories, hooks |
.opencode/agents/CMZ.json |
CMZ-specific configuration |
Adding a New Agent
- Define in
.opencode/oh-my-opencode.json:
"agents": {
"new_agent": {
"model": "opencode/model-name",
"category": "category-name"
}
}- Add to CMZ.json if orchestrator integration needed
Adding a New Skill
- Create directory:
.opencode/skills/skill-name/ - Create
SKILL.mdwith frontmatter (name, description) - Add references in
references/if needed - Follow skill-creator guidelines for structure
Self-* Capabilities
Self-Heal Implementation
Error → Detect → Diagnose → Recover → Learn- Detect: Monitor for exceptions, failed tests, CI failures
- Diagnose: Use systematic-debugging skill, analyze stack traces
- Recover: Implement fix, verify with tests
- Learn: Document in memory, prevent recurrence
Self-Learn Implementation
Feedback → Analyze → Extract → Apply- Collect: User feedback, test outcomes, performance metrics
- Analyze: Identify patterns, successful approaches
- Extract: Generalize into reusable patterns
- Apply: Update skills, configs, documentation
Self-Evolve Implementation
Evaluate → Identify → Implement → Verify- Evaluate: Current capabilities vs requirements
- Identify: Gaps, optimization opportunities
- Implement: New skills, improved delegation
- Verify: Tests pass, metrics improve
Agent Improvement Workflow
RESEARCH → PLAN → IMPLEMENT → VERIFY → SELF-REVIEW → DELIVER
- RESEARCH: Explore codebase, gather context
- PLAN: Create detailed work breakdown
- IMPLEMENT: Make atomic changes
- VERIFY: Run tests, lint, type-check
- SELF-REVIEW: Check against requirements
- DELIVER: Create PR with proper labeling
Verification Checklist
- All tests pass (
npm test) - Lint passes with zero warnings (
npm run lint) - Type check passes (
npm run type-check) - Build succeeds (
npm run build) - Documentation updated if needed
- PR has
ai-agent-engineerlabel
Common Tasks
Improve Agent Configuration
- Read current config from
.opencode/oh-my-opencode.json - Identify improvement (model change, capability addition)
- Make atomic change
- Verify with
opencodeCLI if possible - Document change in commit message
Create New Skill
- Use
skill-creatorskill for guidance - Run
init_skill.pyfrom skill-creator - Write SKILL.md with clear frontmatter
- Add references for detailed content
- Validate and test
Fix Agent Issue
- Reproduce issue
- Use
systematic-debuggingskill - Implement minimal fix
- Add regression test
- Verify fix
Reference Files
For detailed patterns and examples, see:
- Agent Architecture Details: Deep dive into agent system internals, model selection, and advanced patterns
Anti-Patterns
| Anti-Pattern | Why Bad | Instead |
|---|---|---|
| Direct main commits | Bypasses review | Use feature branches |
| Skipping tests | Regressions | Always run tests |
| Large atomic changes | Hard to review | Small, focused changes |
| Ignoring CI failures | Ships bugs | Fix all failures |
| Undocumented changes | Knowledge loss | Update documentation |
Source: cpa03/ai-first — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review