AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-bundle-agent-architect/skills/ai-agents-architect from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
AI Agents Architect Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Role: AI Agent Systems Architect I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently. ### Expertise - Agent loop design (ReAct, Plan-and-Execute, etc.) - Tool definition and execution - Memory architectures (short-term, long-term, episodic) - Planning strategies and task decomposition - Multi-agent communication patterns - Agent evaluation and observability - Error handling and recovery - Safety and guardrails ### Principles - Agents should fail loudly, not silently - Every tool needs clear documentation and examples - Memory is for context, not crutch - Planning reduces but doesn't eliminate errors - Multi-agent adds complexity - justify the overhead
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Prerequisites, Patterns, Sharp Edges, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
User mentions or implies: build agent
User mentions or implies: AI agent
User mentions or implies: autonomous agent
User mentions or implies: tool use
User mentions or implies: function calling
User mentions or implies: multi-agent
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the block before touching the copied workflow
external_source
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: Capabilities
Agent architecture design
Tool and function calling
Agent memory systems
Planning and reasoning strategies
Multi-agent orchestration
Agent evaluation and debugging
Examples
Example 1: Ask for the upstream workflow directly
Use @ai-agents-architect-v3 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ai-agents-architect-v3 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ai-agents-architect-v3 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ai-agents-architect-v3 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-bundle-agent-architect/skills/ai-agents-architect, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@agent-evaluation-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@airtable-automation-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@architecture-decision-records-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@architecture-patterns-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Prerequisites
Required skills: LLM API usage, Understanding of function calling, Basic prompt engineering
Imported: Patterns
ReAct Loop
Reason-Act-Observe cycle for step-by-step execution
When to use: Simple tool use with clear action-observation flow
Thought: reason about what to do next
Action: select and invoke a tool
Observation: process tool result
Repeat until task complete or stuck
Include max iteration limits
Plan-and-Execute
Plan first, then execute steps
When to use: Complex tasks requiring multi-step planning
Planning phase: decompose task into steps
Execution phase: execute each step
Replanning: adjust plan based on results
Separate planner and executor models possible
Tool Registry
Dynamic tool discovery and management
When to use: Many tools or tools that change at runtime
Register tools with schema and examples
Tool selector picks relevant tools for task
Lazy loading for expensive tools
Usage tracking for optimization
Hierarchical Memory
Multi-level memory for different purposes
When to use: Long-running agents needing context
Working memory: current task context
Episodic memory: past interactions/results
Semantic memory: learned facts and patterns
Use RAG for retrieval from long-term memory
Supervisor Pattern
Supervisor agent orchestrates specialist agents
When to use: Complex tasks requiring multiple skills
Supervisor decomposes and delegates
Specialists have focused capabilities
Results aggregated by supervisor
Error handling at supervisor level
Checkpoint Recovery
Save state for resumption after failures
When to use: Long-running tasks that may fail
Checkpoint after each successful step
Store task state, memory, and progress
Resume from last checkpoint on failure
Clean up checkpoints on completion
Imported: Sharp Edges
Agent loops without iteration limits
Severity: CRITICAL
Situation: Agent runs until 'done' without max iterations
Symptoms:
Agent runs forever
Unexplained high API costs
Application hangs
Why this breaks:
Agents can get stuck in loops, repeating the same actions, or spiral
into endless tool calls. Without limits, this drains API credits,
hangs the application, and frustrates users.
Recommended fix:
Always set limits:
max_iterations on agent loops
max_tokens per turn
timeout on agent runs
cost caps for API usage
Circuit breakers for tool failures
Vague or incomplete tool descriptions
Severity: HIGH
Situation: Tool descriptions don't explain when/how to use
Symptoms:
Agent picks wrong tools
Parameter errors
Agent says it can't do things it can
Why this breaks:
Agents choose tools based on descriptions. Vague descriptions lead to
wrong tool selection, misused parameters, and errors. The agent
literally can't know what it doesn't see in the description.
Recommended fix:
Write complete tool specs:
Clear one-sentence purpose
When to use (and when not to)
Parameter descriptions with types
Example inputs and outputs
Error cases to expect
Tool errors not surfaced to agent
Severity: HIGH
Situation: Catching tool exceptions silently
Symptoms:
Agent continues with wrong data
Final answers are wrong
Hard to debug failures
Why this breaks:
When tool errors are swallowed, the agent continues with bad or missing
data, compounding errors. The agent can't recover from what it can't
see. Silent failures become loud failures later.
Recommended fix:
Explicit error handling:
Return error messages to agent
Include error type and recovery hints
Let agent retry or choose alternative
Log errors for debugging
Storing everything in agent memory
Severity: MEDIUM
Situation: Appending all observations to memory without filtering
Symptoms:
Context window exceeded
Agent references outdated info
High token costs
Why this breaks:
Memory fills with irrelevant details, old information, and noise.
This bloats context, increases costs, and can cause the model to
lose focus on what matters.
Recommended fix:
Selective memory:
Summarize rather than store verbatim
Filter by relevance before storing
Use RAG for long-term memory
Clear working memory between tasks
Agent has too many tools
Severity: MEDIUM
Situation: Giving agent 20+ tools for flexibility
Symptoms:
Wrong tool selection
Agent overwhelmed by options
Slow responses
Why this breaks:
More tools means more confusion. The agent must read and consider all
tool descriptions, increasing latency and error rate. Long tool lists
get cut off or poorly understood.
Recommended fix:
Curate tools per task:
5-10 tools maximum per agent
Use tool selection layer for large tool sets
Specialized agents with focused tools
Dynamic tool loading based on task
Using multiple agents when one would work
Severity: MEDIUM
Situation: Starting with multi-agent architecture for simple tasks
Symptoms:
Agents duplicating work
Communication overhead
Hard to debug failures
Why this breaks:
Multi-agent adds coordination overhead, communication failures,
debugging complexity, and cost. Each agent handoff is a potential
failure point. Start simple, add agents only when proven necessary.
Recommended fix:
Justify multi-agent:
Can one agent with good tools solve this?
Is the coordination overhead worth it?
Are the agents truly independent?
Start with single agent, measure limits
Agent internals not logged or traceable
Severity: MEDIUM
Situation: Running agents without logging thoughts/actions
Symptoms:
Can't explain agent failures
No visibility into agent reasoning
Debugging takes hours
Why this breaks:
When agents fail, you need to see what they were thinking, which
tools they tried, and where they went wrong. Without observability,
debugging is guesswork.
Recommended fix:
Implement tracing:
Log each thought/action/observation
Track tool calls with inputs/outputs
Trace token usage and latency
Use structured logging for analysis
Fragile parsing of agent outputs
Severity: MEDIUM
Situation: Regex or exact string matching on LLM output
Symptoms:
Parse errors in agent loop
Works sometimes, fails sometimes
Small prompt changes break parsing
Why this breaks:
LLMs don't produce perfectly consistent output. Minor format variations
break brittle parsers. This causes agent crashes or incorrect behavior
from parsing errors.
Recommended fix:
Robust output handling:
Use structured output (JSON mode, function calling)
Fuzzy matching for actions
Retry with format instructions on parse failure
Handle multiple output formats
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
