AgentSkillsCN

agent-architect

一种元技能，用于设计、搭建框架并实现新的Claude Code智能体/技能。

SKILL.md
--- frontmatter
name: agent-architect
description: A meta-skill for designing, scaffolding, and implementing new Claude Code Agents/Skills.
argument-hint: [new-agent-name] [goal]

Agent Architect

Goal

To construct robust, production-grade AI agents by strictly following the Plan-and-Solve (SPARC) methodology.

Workflow

1. Specification (The "Plan")

  1. •Ask: Clarify inputs, outputs, and constraints.
  2. •Draft: Create [agent-name]/SPEC.md using templates/SPEC_TEMPLATE.md.
  3. •Review: Wait for user approval.

2. Architecture (The "Skeleton")

  1. •Scaffold: Create directories (scripts, references, tests).
  2. •Generate: Create [agent-name]/SKILL.md using templates/SKILL_TEMPLATE.md.
  3. •Code Quality Setup: For Python projects, set up code quality tools:
    • •Create pyproject.toml using templates/pyproject.toml.template
    • •Create .pre-commit-config.yaml using templates/.pre-commit-config.yaml
    • •Create tests/conftest.py using templates/conftest.py

3. Implementation (The "Solve")

  1. •Scripting: Write complex logic in [agent-name]/scripts/ (Python/Bash).
  2. •Refinement: Update SKILL.md to use these scripts.
  3. •Code Quality: For Python implementations:
    • •Type Hints: Ensure all public functions have complete type annotations (PEP 484, 585, 604)
    • •Docstrings: Add Google-style docstrings to all public APIs (PEP 257)
    • •Error Handling: Implement custom exception hierarchy and use context managers

4. Verification

  1. •Format: For Python code, run black and isort on all source files
  2. •Type Check: Run mypy --strict and fix type errors
  3. •Lint: Check YAML frontmatter and run ruff check for Python
  4. •Test: Run pytest with 80%+ coverage target
  5. •Security: Run safety check on Python dependencies
  6. •Dry Run: Perform a final validation of the agent

Code Quality Standards

For Python projects, follow comprehensive coding standards:

  • •Reference: See Python coding standards for detailed guidelines
  • •Formatting: Black (100 char line length), isort
  • •Type Checking: Mypy strict mode with complete type hints
  • •Testing: Pytest with 80%+ coverage, fixtures, parametrized tests
  • •Security: No secrets in code, parameterized queries, security scanning

Design Patterns

Apply appropriate design patterns to maintain clean architecture:

  • •Creational: Factory Pattern (for connector creation), Builder Pattern (for configuration)
  • •Structural: Adapter Pattern (database adapters), Decorator Pattern (tracing, metrics)
  • •Behavioral: Strategy Pattern (query execution), Iterator Pattern (streaming), Observer Pattern (events)

Tools & Scripts

  • •scripts/lint_check.sh: Run all code quality checks (black, isort, mypy, ruff, safety)
  • •scripts/run_tests.sh: Run pytest with coverage reporting and various options

Templates

  • •templates/SPEC_TEMPLATE.md: Specification template
  • •templates/SKILL_TEMPLATE.md: Skill documentation template
  • •templates/pyproject.toml.template: Python project configuration
  • •templates/.pre-commit-config.yaml: Pre-commit hooks configuration
  • •templates/test_template.py: Pytest test examples
  • •templates/conftest.py: Pytest configuration and shared fixtures