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codehealth-mcp

Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs. Use when reviewing code quality, refactoring, checking if AI changes degraded a file, or before commit/PR.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=affaan-m-ecc-skills-codehealth-mcp-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name codehealth-mcp description Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs. Use when reviewing code quality, refactoring, checking if AI changes degraded a file, or before commit/PR. metadata {"origin":"community"} Code Health MCP (CodeScene) Structural maintainability feedback for AI-assisted coding. Complements style/lint skills ( coding-standards , plankton-code-quality ) with design-level health scores and regression gates. Upstream: codescene-oss/codescene-mcp-server Package: @codescene/codehealth-mcp (stdio via npx) Security and boundaries Opt-in (ECC): The codescene block in mcp-configs/mcp-servers.json is a template only. ECC plugin installs do not auto-enable bundled MCP servers. Copy the entry into your config only if you want it. You can exclude it during ECC install/sync with ECC_DISABLED_MCPS=codescene,... . Credentials: No bundled token. Set CS_ACCESS_TOKEN yourself (see getting-a-personal-access-token.md in the upstream repo). Never commit tokens to the repo. What the tools read: When invoked, tools analyze files and git state in the local repository you point them at (paths you pass, plus branch context for analyze_change_set ). They do not run by themselves. For standalone mode, follow upstream privacy docs: codescene-mcp-server README and CodeScene policies . Do not use this skill for secrets, credentials, or paths you do not want analyzed. If the MCP is unavailable (offline, bad token, server crash): Do not invent Code Health scores. Tell the user the check was skipped. Continue only with explicit user approval. Prefer lint/tests/verification-loop for gating when MCP is down. Re-enable checks once the server connects. When to Use User asks to review code quality , refactor a file, or check if AI changes degraded maintainability Before editing a hotspot , legacy module, or unfamiliar file Before commit or pull request when you need a maintainability safeguard After a large agent-written diff — verify Code Health did not regress Pair with verification-loop , tdd-workflow , or /quality-gate as a structural check (not a replacement for tests/lint) When to Activate Same triggers as When to Use above — this heading is what ECC uses for skill auto-activation. How It Works 1. Connect the MCP server Copy the codescene entry from mcp-configs/mcp-servers.json into your harness MCP config. Claude Code ( ~/.claude.json → mcpServers ): "codescene" : { "command" : "npx" , "args" : [ "-y" , "@codescene/codehealth-mcp" ] , "env" : { "CS_ACCESS_TOKEN" : "YOUR_CS_ACCESS_TOKEN_HERE" } } Project-scoped: merge the same block into .mcp.json at the repo root. Token setup is documented in the upstream repo (link above). Standalone mode does not require a paid CodeScene platform account for the four tools listed below. Restart the session and confirm the codescene server is connected before relying on scores. 2. Call standalone tools only Tool When to use code_health_review Full structural analysis before modifying a file code_health_score Quick numeric score after each change (delta check) pre_commit_code_health_safeguard Block commits that introduce Code Health regressions analyze_change_set Branch-level check before opening a PR Do not call platform-only tools (e.g. repository-wide technical debt hotspot lists). Do not reference delta_analysis — not available on standalone. 3. Interpret scores (1–10) Range Meaning Agent behavior 9.0–10.0 Green — healthy Safer to extend; still prefer vertical slices 4.0–8.9 Yellow — debt Tread carefully; no drive-by refactors 1.0–3.9 Red — severe debt Narrow scope only 4. Run the feedback loop Before touching a file Run code_health_review on the target path. Record baseline score and listed code smells. Plan the smallest change that addresses the task. Scope by score: below 5 — minimal diff only; 5–7 — no broad refactors; above 7 — safer to refactor, still verify after each edit. After each change Run code_health_score on the same file. Compare to the baseline from code_health_review . If the score regressed , fix before continuing. Never mark the task done while the score is lower than when you started. Before every commit — run pre_commit_code_health_safeguard on the repository path. Before a PR — run analyze_change_set against the base branch (e.g. main ). Examples Example: Flask maintainability improvement On pallets/flask , an agent loop using only standalone tools: code_health_review on a target module (baseline 4.82 ) Targeted refactor addressing listed smells code_health_score after each edit pre_commit_code_health_safeguard before commit analyze_change_set before PR Result: Code Health 4.82 → 9.1 (free standalone token only). Example: AGENTS.md enforcement block Paste into the project AGENTS.md or CLAUDE.md : ## Code Health (CodeScene MCP) Before modifying any file: run `code_health_review` , note score and issues. - Score below 5: problematic range — scope changes narrowly. - Score 5–7: warning range — no broad refactors. After each change: run `code_health_score` to verify delta. - If score regressed: fix before continuing; never declare done if score dropped. Before every commit: run `pre_commit_code_health_safeguard` . Before PR: run `analyze_change_set` . Example: anti-patterns vs correct loop # BAD: Edit first, check later [large refactor without code _health_ review] # BAD: Ignore score drop "Tests pass" → mark task done while Code Health decreased # BAD: Broad refactor on red-score file (below 5) Drive-by cleanup across the module # GOOD: review → small change → score → commit safeguard → analyze _change_ set Pairing with ECC ECC skill / flow Code Health MCP role coding-standards Style/naming; Code Health = structure/complexity plankton-code-quality Write-time lint/format; Code Health = pre/post edit structural gate verification-loop / /quality-gate Add structural regression check before "done" security-review Security vs maintainability — use both when relevant tdd-workflow Tests pass ≠ healthy design — check score after refactors Context tip: ECC recommends keeping MCP count low. Enable codescene when doing substantive edits; disable when not needed. Related Skills coding-standards — baseline conventions plankton-code-quality — write-time lint/format hooks verification-loop — build/test/lint gate tdd-workflow — test-first development security-review — security checklist documentation-lookup — library docs via Context7 (orthogonal)
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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