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LLM token logprobs and calibration. Per-decision confidence, ECE, Brier, reliability diagrams, low-confidence triage.
Analyze LLM token logprobs and calibration. Use for per-decision confidence, ECE, Brier scores, reliability diagrams, and low-confidence triage.
回顾最近 N 天的 Claude Code 使用记录——扫描原始会话数据,按主题分组汇总"我都做了什么",并从个人操作系统视角输出模式、风险与增删建议。当用户说 /recap、"看看我这几天做了什么"、"回顾一下我最近的会话"、"这两天我用 claude 干了啥"、"活动回顾" 时使用。
正在显示 SKILL.md
基于 SOC 职业分类
| name | call-gemini |
| description | Invoke the Gemini CLI for architecture and scalability review. |
| version | 1.1.0 |
| tags | ["gemini","architecture","review","validation"] |
| owner | orchestration |
| status | active |
Wrapper for invoking Gemini agent via CLI for architecture and scalability review.
Gemini is specialized for:
gemini CLI installed and accessible on PATH./call-gemini
| Area | Weight | Description |
|---|---|---|
| Architecture | 0.7 | Design patterns, modularity |
| Scalability | 0.8 | Performance at scale, bottlenecks |
| Patterns | 0.6 | Design patterns, anti-patterns |
gemini --yolo "<prompt>"
| Flag | Purpose |
|---|---|
--yolo | Auto-approve tool calls (required for non-interactive) |
--model | Select model (optional, default: gemini-2.0-flash) |
IMPORTANT:
--output-formatgemini --yolo "
You are reviewing an implementation plan for architecture and scalability.
Read the plan JSON provided by the orchestrator (phase_outputs export).
Evaluate for:
1. Architecture patterns - Are they appropriate for the use case?
2. Modularity - Is the design properly decomposed?
3. Scalability - Will this scale with increased load?
4. Maintainability - Is the code easy to maintain and extend?
5. Technical debt - Does this introduce unnecessary complexity?
6. Design principles - SOLID, DRY, KISS compliance
Return your assessment as JSON (wrap in code block):
\`\`\`json
{
\"agent\": \"gemini\",
\"phase\": \"validation\",
\"approved\": true|false,
\"score\": 1-10,
\"assessment\": \"Brief summary of your review\",
\"architecture_review\": {
\"patterns_identified\": [\"List of patterns found\"],
\"patterns_recommended\": [\"Patterns that should be used\"],
\"anti_patterns\": [\"Any anti-patterns detected\"]
},
\"concerns\": [
{
\"area\": \"architecture|scalability|maintainability\",
\"severity\": \"high|medium|low\",
\"description\": \"Specific concern\",
\"recommendation\": \"How to address it\"
}
],
\"blocking_issues\": [\"Issues that MUST be fixed\"],
\"strengths\": [\"Positive aspects of the design\"]
}
\`\`\`
Focus on design quality. Only block for fundamental architecture flaws.
"
gemini --yolo "
You are performing an architecture-focused code review of recently implemented changes.
Review the implementation for:
1. Architecture compliance - Does code match the plan?
2. Design pattern correctness - Are patterns implemented correctly?
3. Separation of concerns - Is responsibility properly divided?
4. Coupling and cohesion - Are modules appropriately connected?
5. Extensibility - Can this be easily extended?
6. Performance implications - Any obvious bottlenecks?
Also check:
- Code organization and structure
- Naming conventions and clarity
- Documentation adequacy
- Error handling patterns
- Resource management
Return your assessment as JSON (wrap in code block):
\`\`\`json
{
\"agent\": \"gemini\",
\"phase\": \"verification\",
\"approved\": true|false,
\"score\": 1-10,
\"assessment\": \"Brief summary of code review\",
\"architecture_compliance\": {
\"matches_plan\": true|false,
\"deviations\": [\"Any deviations from plan\"]
},
\"issues\": [
{
\"file\": \"path/to/file.py\",
\"concern\": \"What's the issue\",
\"severity\": \"high|medium|low\",
\"category\": \"architecture|scalability|maintainability\",
\"recommendation\": \"How to improve\"
}
],
\"blocking_issues\": [\"Critical architecture issues\"],
\"quality_score\": {
\"modularity\": 1-10,
\"extensibility\": 1-10,
\"maintainability\": 1-10
}
}
\`\`\`
Be constructive. Focus on significant architecture concerns, not style nitpicks.
"
Gemini returns markdown with embedded JSON. Parse with:
import re
import json
# Extract JSON from code block
match = re.search(r'```json\s*(.*?)\s*```', gemini_output, re.DOTALL)
if match:
result = json.loads(match.group(1))
else:
# Try to find raw JSON
match = re.search(r'\{.*\}', gemini_output, re.DOTALL)
if match:
result = json.loads(match.group(0))
logs with type escalation or blockerPhase 2 (Validation):
phase_outputs as gemini_feedbackPhase 4 (Verification):
phase_outputs as gemini_review| Phase | Min Score | Blocking Issues |
|---|---|---|
| Validation | 6.0 | None allowed |
| Verification | 7.0 | None allowed |
# From project directory
cd projects/my-app
# Run validation
gemini --yolo "Review the plan JSON provided in this prompt for architecture..."
# Parse the output (may need to extract JSON from markdown)
cat gemini-output.txt
When Gemini disagrees with Cursor:
| Issue Type | Resolution |
|---|---|
| Architecture concern | Gemini wins (weight 0.7) |
| Security concern | Cursor wins (weight 0.8) |
| Code quality | Average scores |
| Both blocking | Human escalation |
# Use specific model
gemini --model gemini-2.0-flash --yolo "prompt"
# Available models
# - gemini-2.0-flash (default, fastest)
# - gemini-2.0-pro (more thorough)
/validate - Plan validation workflow/verify - Code review workflow/resolve-conflict - Merge Cursor and Gemini feedback