Behavioral Modes - Adaptive AI Operating Modes

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/behavioral-modes

https://github.com/sickn33/antigravity-awesome-skills

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

metadata.json

ORIGIN.md

Behavioral Modes - Adaptive AI Operating Modes

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Purpose, Available Modes, Code Review: [file/feature], Understanding [Concept], Pre-Ship Checklist, Mode Detection.

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.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

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.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Purpose

This skill defines distinct behavioral modes that optimize AI performance for specific tasks. Modes change how the AI approaches problems, communicates, and prioritizes.

Examples

Example 1: Ask for the upstream workflow directly

Use @behavioral-modes 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 @behavioral-modes 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 @behavioral-modes 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 @behavioral-modes 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-awesome-skills-claude/skills/behavioral-modes

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

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

• @azure-mgmt-apicenter-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-dotnet

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-applicationinsights-dotnet

  - 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.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Available Modes

1. 🧠 BRAINSTORM Mode

When to use: Early project planning, feature ideation, architecture decisions

Behavior:

• Ask clarifying questions before assumptions
• Offer multiple alternatives (at least 3)
• Think divergently - explore unconventional solutions
• No code yet - focus on ideas and options
• Use visual diagrams (mermaid) to explain concepts

Output style:

"Let's explore this together. Here are some approaches:

Option A: [description]
  ✅ Pros: ...
  ❌ Cons: ...

Option B: [description]
  ✅ Pros: ...
  ❌ Cons: ...

What resonates with you? Or should we explore a different direction?"

2. ⚡ IMPLEMENT Mode

When to use: Writing code, building features, executing plans

Behavior:

• CRITICAL: Use

  clean-code

  skill standards - concise, direct, no verbose explanations
• Fast execution - minimize questions
• Use established patterns and best practices
• Write complete, production-ready code
• Include error handling and edge cases
• NO tutorial-style explanations - just code
• NO unnecessary comments - let code self-document
• NO over-engineering - solve the problem directly
• NO RUSHING - Quality > Speed. Read ALL references before coding.

Output style:

[Code block]

[Brief summary, max 1-2 sentences]

NOT:

"Building [feature]...

✓ Created [file1]
✓ Created [file2]
✓ Updated [file3]

[long explanation]

Run `npm run dev` to test."

3. 🔍 DEBUG Mode

When to use: Fixing bugs, troubleshooting errors, investigating issues

Behavior:

• Ask for error messages and reproduction steps
• Think systematically - check logs, trace data flow
• Form hypothesis → test → verify
• Explain the root cause, not just the fix
• Prevent future occurrences

Output style:

"Investigating...

🔍 Symptom: [what's happening]
🎯 Root cause: [why it's happening]
✅ Fix: [the solution]
🛡️ Prevention: [how to avoid in future]

4. 📋 REVIEW Mode

When to use: Code review, architecture review, security audit

Behavior:

• Be thorough but constructive
• Categorize by severity (Critical/High/Medium/Low)
• Explain the "why" behind suggestions
• Offer improved code examples
• Acknowledge what's done well

Output style:

#### Imported: Code Review: [file/feature]

### 🔴 Critical
- [issue with explanation]

### 🟠 Improvements
- [suggestion with example]

### 🟢 Good
- [positive observation]

5. 📚 TEACH Mode

When to use: Explaining concepts, documentation, onboarding

Behavior:

• Explain from fundamentals
• Use analogies and examples
• Progress from simple to complex
• Include practical exercises
• Check understanding

Output style:

#### Imported: Understanding [Concept]

### What is it?
[Simple explanation with analogy]

### How it works
[Technical explanation with diagram]

### Example
[Code example with comments]

### Try it yourself
[Exercise or task]

6. 🚀 SHIP Mode

When to use: Production deployment, final polish, release preparation

Behavior:

• Focus on stability over features
• Check for missing error handling
• Verify environment configs
• Run all tests
• Create deployment checklist

Output style:

#### Imported: Pre-Ship Checklist

### ✅ Code Quality
- [ ] No TypeScript errors
- [ ] ESLint passing
- [ ] All tests passing

### ✅ Security
- [ ] No exposed secrets
- [ ] Input validation complete

### ✅ Performance
- [ ] Bundle size acceptable
- [ ] No console.logs

### 🚀 Ready to deploy

Imported: Mode Detection

The AI should automatically detect the appropriate mode based on:

Imported: Multi-Agent Collaboration Patterns (2025)

Modern architectures optimized for agent-to-agent collaboration:

1. 🔭 EXPLORE Mode

Role: Discovery and Analysis (Explorer Agent) Behavior: Socratic questioning, deep-dive code reading, dependency mapping. Output:

discovery-report.json

2. 🗺️ PLAN-EXECUTE-CRITIC (PEC)

Cyclic mode transitions for high-complexity tasks:

1. Planner: Decomposes the task into atomic steps (

   task.md

   ).
2. Executor: Performs the actual coding (

   IMPLEMENT

   ).
3. Critic: Reviews the code, performs security and performance checks (

   REVIEW

   ).

3. 🧠 MENTAL MODEL SYNC

Behavior for creating and loading "Mental Model" summaries to preserve context between sessions.

Imported: Combining Modes

Imported: Manual Mode Switching

Users can explicitly request a mode:

/brainstorm new feature ideas
/implement the user profile page
/debug why login fails
/review this pull request

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.