菜单
BAIME-aligned refactoring protocol for Go hotspots (CLIs, services, MCP tooling) with automated metrics (e.g., metrics-cli, metrics-mcp) and documentation.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
Use this skill whenever the user wants high-quality semantic retrieval, cross-textbook synthesis, reference verification, or authority-ranked question answering over the PageIndex corpus. Trigger on requests like "find which texts cover X", "compare sources", "retrieve semantically", "verify this claim from my textbooks", "build a reading list from the corpus", "cluster by topic", or any PageIndex question where naïve filename search would miss relevant material. This skill teaches a state-of-the-art hybrid retrieval workflow over the PageIndex catalog and live MCP extraction surface.
Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming. Use when you need to build complex AI systems, program LMs declaratively, optimize prompts automatically, create modular AI pipelines, or build RAG systems and agents.
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
Real-time observability dashboard for multi-agent Claude Code sessions. Visualize agent interactions, tool usage, and session flows in real-time through a web dashboard. Track multiple agents running in parallel with swim lane visualization, event filtering, and live charts. **Key Features:** - 🔴 Real-time event streaming via WebSocket - 📊 Agent swim lanes showing parallel execution - 🔍 Event filtering by agent, session, event type - 📈 Live charts for tool usage patterns - 💾 Filesystem-based (no database required) **Inspired by [@indydevdan](https://github.com/indydevdan)**'s work on multi-agent observability. **Our approach:** Filesystem + in-memory streaming vs. indydevdan's SQLite database approach.
Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
Routes tasks to appropriate Task tool agents (subagents). Triggers on complex multi-step tasks, research, code review, exploration, or any task benefiting from specialized agent execution. Matches intent to agent types.
| name | Code Refactoring |
| description | BAIME-aligned refactoring protocol for Go hotspots (CLIs, services, MCP tooling) with automated metrics (e.g., metrics-cli, metrics-mcp) and documentation. |
| allowed-tools | Read, Write, Edit, Bash, Grep, Glob |
λ(target_pkg, target_hotspot, metrics_target) → (refactor_plan, metrics_snapshot, validation_report) | ∧ configs = read_json(experiment-config.json)? ∧ catalogue = configs.metrics_targets ∨ [] ∧ require(cyclomatic(target_hotspot) > 8) ∧ require(catalogue = [] ∨ metrics_target ∈ catalogue) ∧ require(run("make " + metrics_target)) ∧ baseline = results.md ∧ iterations/ ∧ apply(pattern_set = reference/patterns.md) ∧ use(templates/{iteration-template.md,refactoring-safety-checklist.md,tdd-refactoring-workflow.md,incremental-commit-protocol.md}) ∧ automate(metrics_snapshot) via scripts/{capture-*-metrics.sh,count-artifacts.sh} ∧ document(knowledge) → knowledge/{patterns,principles,best-practices} ∧ ensure(complexity_delta(target_hotspot) ≥ 0.30 ∧ cyclomatic(target_hotspot) ≤ 10) ∧ ensure(coverage_delta(target_pkg) ≥ 0.01 ∨ coverage(target_pkg) ≥ 0.70) ∧ validation_report = validate-skill.sh → {inventory.json, V_instance ≥ 0.85}
