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code-enhancer-workflow
Parallel execution workflow for code enhancement and multi-domain deep-dive auditing using the Unified Parallel Engine
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Parallel execution workflow for code enhancement and multi-domain deep-dive auditing using the Unified Parallel Engine
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Scaffold a complete agent-package project with all config files, Docker infrastructure, MCP server, A2A agent, and API client stubs. Use when creating a brand-new agent-package from scratch, bootstrapping a new MCP/agent/api-client project, or when the user says "create a new agent package". This delegates domain-specific implementation to existing skills (api-client-builder, mcp- builder, agent-builder, skill-graph-builder). Do NOT use for modifying an existing agent package — use the individual skills directly.
Browser automation CLI for AI agents using the agent-browser tool. Use when the user needs to interact with websites including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
Guide for building scalable Pydantic AI agents. Use this skill when the user wants to create a new agent package or modify an existing agent's architecture, to ensure it follows the standardized agent patterns using `agent-utilities`.
The `agent-spawner` skill provides the ability to dynamically create a new Pydantic AI agent configured with an external MCP server toolkit. This is extremely useful when an agent wants to spin up a sub-agent with specialized tools defined in an `mcp_config.json`, and interact with that agent immediately without modifying the parent agent's state or toolset.
Consolidated skill for managing, dispatching, and orchestrating other agents via the agent-manager CLI, as well as workflows for A2A communication, orchestration, and parallel subagent dispatch.
Use when executing implementation plans with independent tasks in the current session
| name | code-enhancer-workflow |
| skill_type | workflow |
| description | Parallel execution workflow for code enhancement and multi-domain deep-dive auditing using the Unified Parallel Engine |
| domain | development-workflows |
| agent | dev_ops_engineer |
| team_config | {"name":"code_enhancement_team","task_pattern":"code quality, security, and traceability auditing","execution_mode":"parallel","specialist_ids":["analysis-agent","security-agent","test-agent","doc-agent","synthesis-agent"],"tool_assignments":{"analysis-agent":["rep_rm_workspace","rep_rm_projects"],"security-agent":["rep_rm_projects"],"test-agent":["rep_rm_projects"],"doc-agent":["rep_rm_workspace"],"synthesis-agent":["graph_write","rep_rm_git"]}} |
| tags | ["dev-workflows","code-enhancer"] |
| concept | CONCEPT:DEV-002 |
| metadata | {"version":"1.2.1"} |
CONCEPT:DEV-002
Parallel execution workflow for code enhancement and multi-domain deep-dive auditing using the Unified Parallel Engine
Agent: analysis-agent
Tools: rep_rm_workspace, rep_rm_projects
Detect primary/secondary languages, build systems, available linters, and test frameworks in the target project.
Expected: detect_language_artifacts
Agent: analysis-agent
Tools: rep_rm_workspace, rep_rm_projects
Scan codebase for architectural patterns, libraries, Pydantic-AI agent setups, externalized prompts, and dependency freshness.
Expected: project_analysis_artifacts
Agent: analysis-agent
Tools: rep_rm_projects
Orchestrate pre-commit hooks and language-appropriate linters (ruff, mypy, bandit, eslint, go vet) to scan for code issues.
Expected: run_linters_artifacts
Agent: test-agent
Tools: rep_rm_projects
Locate and execute the project test suite with automated test framework detection and timeout protections.
Expected: run_tests_artifacts
Agent: analysis-agent
Tools: rep_rm_projects
Examine module coupling, cyclomatic complexity, directory density, code duplication, and smell patterns.
Expected: deep_code_analysis_artifacts
Agent: security-agent
Tools: rep_rm_projects
Evaluate attack surface, credential exposure, CWE vulnerability patterns, threat model alignments, and API/SSO security controls.
Expected: security_analysis_artifacts
Agent: doc-agent
Tools: rep_rm_workspace
Audit repository documentation (README.md, AGENTS.md, docs/) for quality, completeness, Keep a Changelog standard compliance, and drift.
Expected: documentation_audit_artifacts
Agent: doc-agent
Tools: rep_rm_workspace
Scan codebase docstrings, test suites, and documentation files for CONCEPT ID annotations and map traceability/parity.
Expected: concept_traceability_audit_artifacts
Agent: analysis-agent
Tools: rep_rm_workspace
Evaluate web or terminal interface files against Usability Heuristics and WCAG accessibility standards.
Expected: user_interface_analysis_artifacts
Agent: synthesis-agent
Tools: rep_rm_workspace
Consolidate the multi-domain findings into a prettified 0-100 graded report with prioritized TODOs and structured SDD handoffs.
Expected: generate_report_artifacts
Agent: synthesis-agent
Tools: graph_write
Persist the generated report, grading metrics, and identified conceptual/code linkages in Graph-OS Knowledge Graph.
Expected: kg_persistence_artifacts
.specify/Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.
Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.