Agent Reviewer Protocol

Task is done — now look back. What went well, what went wrong, what should be different next time? Goal: never repeat the same mistake and continuously improve skills and processes.

Core principle: Retrospectives are painful but necessary. A good agent evaluates itself.

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6 Review Dimensions

1. Goal Alignment

Did the result match the original intent?

• Was the user's actual request met?
• Did scope creep occur?
• Over-delivery or under-delivery?

2. Efficiency

Did the task take longer than necessary?

• Unnecessary tool calls?
• Repeated operations?
• Sequential steps that could have been parallel?
• Token/resource waste?

3. Decision Quality

Were decisions well-reasoned?

• Were assumptions verified?
• Were alternatives considered?
• Did early decisions cause later problems?

4. Error Handling

How were errors addressed?

• Detected quickly?
• Right strategy applied?
• Same error repeated?

5. Communication

How was user interaction quality?

• Unnecessary confirmations requested?
• Critical information missing at key points?
• Too many or too few questions?

6. Reusability

Can lessons from this task transfer to the next?

• General patterns discovered?
• Which skills were missing or insufficient?
• Which decisions should become standard?

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Finding Severity

Severity	Meaning	Action
CRITICAL	Endangered the task or significantly reduced quality	Must fix
MODERATE	Created inefficiency but didn't break the result	Improve
POSITIVE	Something that went better than expected	Repeat, standardize

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Output Format

AGENT REVIEWER — Task Retrospective
Task     : [task name]
Score    : X/10
Findings : N critical | N moderate | N positive

## Dimension Scores

| Dimension | Score | Summary |
|-----------|-------|---------|
| Goal Alignment | X/10 | ... |
| Efficiency | X/10 | ... |
| Decision Quality | X/10 | ... |
| Error Handling | X/10 | ... |
| Communication | X/10 | ... |
| Reusability | X/10 | ... |
| **Overall** | **X/10** | |

## Critical Findings
[If any — what happened, why critical, how to prevent]

## Improvement Areas
[Inefficiencies, missed opportunities]

## What Went Well
[Decisions and approaches worth repeating]

## Action Items

### For Next Task
1. [Concrete change — what to do]
2. [Concrete change]

### Skill / Process Improvement
1. [Which skill should be updated / added]
2. [Which pattern should be standardized]

## Lessons Learned
[Items a future agent instance should know — candidates for memory-ledger]

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Inefficiency Patterns — Auto-Detect

Scan the task history for these patterns:

Pattern	Symptom	Fix
Repeated tool call	Same file/API read 2+ times	Cache it
Unnecessary confirmation	Low-risk step triggered approval	Adjust checkpoint-guardian threshold
Late assumption discovery	"Actually it should be..." after error	Trigger assumption-checker earlier
Sequential parallel steps	Independent steps ran sequentially	Use parallel-planner
Blind retry	Logic error treated as transient	Fix error-recovery categorization
Context loss	Previous step info forgotten	Memory-ledger not updated
Over-decomposition	2-step task split into 8	Adjust task-decomposer granularity

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Skill Performance Evaluation

Evaluate skills used during the task:

## Skills Used

| Skill | Used? | Effective? | Notes |
|-------|-------|------------|-------|
| task-decomposer | Yes/No | Good/Fair/Poor | ... |
| checkpoint-guardian | Yes/No | Good/Fair/Poor | ... |
| assumption-checker | Yes/No | Good/Fair/Poor | ... |
| tool-selector | Yes/No | Good/Fair/Poor | ... |
| parallel-planner | Yes/No | Good/Fair/Poor | ... |
| error-recovery | Yes/No | Good/Fair/Poor | ... |
| memory-ledger | Yes/No | Good/Fair/Poor | ... |
| output-critic | Yes/No | Good/Fair/Poor | ... |

Missing / untriggered skills and why?

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When to Skip

• Task was single-step or under 5 minutes
• Prototype / experimental task
• User said "no retrospective needed"

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Guardrails

• Be honest, not kind — the value is in finding problems, not hiding them.
• Concrete suggestions only — "do better" is useless; "cache file reads to avoid 3 redundant calls" is actionable.
• Cross-skill: this is the ecosystem's feedback loop — findings here should update other skills and processes.