feat: add more skills (from everything-claude-code), config (rules, agents, tools, learning), add new 2 tempalte for dev-team and qc-team

.openclaw/workspace/skills/cost-aware-llm-pipeline/SKILL.md

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+---
+name: cost-aware-llm-pipeline
+description: Cost optimization for AI development pipelines — model routing, budget tracking, token optimization. Use when managing multi-step AI workflows, reducing API costs, or selecting models for tasks.
+---
+
+# Cost-Aware LLM Pipeline
+
+Based on ECC cost-aware-llm-pipeline patterns.
+
+## Model Selection Strategy
+
+Match model capability to task complexity:
+
+| Task Type | Model Tier | Examples |
+|-----------|-----------|----------|
+| Simple (summarize, classify, format) | Small/Fast | Claude Haiku, GPT-4o-mini |
+| Medium (code fixes, analysis, rewrite) | Mid | Claude Sonnet, GPT-4o |
+| Complex (architecture, debugging, planning) | High | Claude Opus, GPT-4o, Gemini Pro |
+| Creative (design, brainstorming) | Variable | Depends on breadth needed |
+
+## Token Optimization Techniques
+
+1. **Trim system prompts**: Remove redundant instructions. Keep only task-specific rules.
+2. **Compress context**: Use summaries instead of full files. Skip irrelevant code.
+3. **Background processes**: Run long tasks (test suites, builds) async. Don't waste tokens waiting.
+4. **Chunk large files**: Read only relevant sections with offset/limit.
+5. **Avoid loops**: Don't poll in a loop. Use proper timeouts and wait mechanisms.
+
+## Budget Tracking
+
+```bash
+# Check session cost (if available)
+session_status
+
+# Monitor token usage
+# Track: input_tokens, output_tokens, cost_cents
+```
+
+## Cost Reduction Rules
+
+- Use the cheapest model that still works
+- Batch similar requests together
+- Cache responses when possible
+- Avoid regenerating the same output
+- Set explicit max_tokens for generation
+- Use structured output (JSON) to reduce retries
+- Prefer targeted file reads over broad "read everything"
+
+## Pipeline Design
+
+When building multi-step AI workflows:
+1. Step 1: Plan/analyze with mid-tier model
+2. Step 2: Implement with appropriate model for code
+3. Step 3: Verify/test with cheapest model
+4. Step 4: Review with high-tier model only if needed