| name | metacognitive-guard |
| description | Monitors Claude's responses for struggle signals and suggests escalation to deep-thinking agents when complexity exceeds comfortable reasoning capacity. |
Metacognitive Guard Skill
This skill provides awareness of the struggle detection system and guidance on when to proactively engage deep-thinking resources.
When to Self-Escalate
Even before the struggle detector triggers, consider spawning deep-think-partner when:
High-Complexity Indicators
-
Architectural decisions with competing constraints
- Multiple valid approaches exist
- Trade-offs span different dimensions (performance, maintainability, cost)
- Decision affects multiple system components
-
Ambiguous requirements requiring interpretation
- User hasn't specified implementation details
- Multiple reasonable interpretations exist
- Wrong choice has significant rework cost
-
Multi-domain synthesis required
- Problem spans multiple technology areas
- Integration patterns aren't obvious
- Prior art doesn't directly apply
-
Edge case analysis needed
- Happy path is clear but edge cases aren't
- Failure modes need systematic exploration
- Concurrency or timing issues involved
Self-Assessment Checklist
Before responding to complex questions, ask yourself:
If you answer "no" to any of these, consider proactive escalation.
How to Escalate
Use the Task tool with the deep-think-partner agent:
Task tool:
subagent_type: deep-think-partner
prompt: [Detailed problem statement with all constraints]
description: [3-5 word summary]
Good Prompts for Deep-Think Partner
Include:
- Context: What system/codebase is this for?
- Constraints: What limits the solution space?
- Success criteria: How do we know we got it right?
- Specific question: What decision needs to be made?
Example Escalation
User asks: "Should we use Redis or PostgreSQL for session storage?"
Self-assessment: Multiple valid approaches, depends on constraints not yet explored, "it depends" isn't helpful.
Escalation:
Task tool:
subagent_type: deep-think-partner
prompt: |
Context: Web application with 10k concurrent users, existing PostgreSQL database.
Question: Redis vs PostgreSQL for session storage.
Constraints: Team has PostgreSQL expertise, no Redis experience.
Must handle session expiry. Cost-sensitive.
Success: Clear recommendation with migration path.
description: Analyze session storage options
Understanding Struggle Signals
The automatic detector looks for these patterns in your responses:
| Signal | What It Means | Better Approach |
|---|
| Hedging | Uncertainty about recommendation | Escalate for deeper analysis |
| Deflecting | Avoiding commitment with questions | Answer then ask clarifying questions |
| Verbose | Rambling without concrete output | Structure response, include code/tables |
| Contradiction | Changed position mid-response | Stop, think, give one coherent answer |
| Apologetic | Previous response was wrong | Acknowledge, correct, move forward |
| Weaseling | Non-committal to avoid being wrong | Make a recommendation with confidence level |
Integration with Deep-Think Partner
When deep-think-partner returns its analysis:
- Don't just paste it - synthesize for the user
- Highlight the key insight - what's the non-obvious finding?
- Present the recommendation clearly - don't bury it
- Offer the implementation plan - if user wants to proceed
Metrics
Track your struggle detection rate to improve:
- How often does the detector trigger?
- Are triggers false positives or genuine struggles?
- Does escalation produce better outcomes?
Self-awareness of your own patterns helps calibrate both the detector and your escalation instincts.
