Codehealth Mcp

作者 affaan-mef648e01899b无许可证275K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库3天前更新

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.

AI 生成的概览

指导智能体使用 CodeScene MCP 服务器进行结构性代码健康度审查、分数变化检查以及提交和 PR 门禁。

功能
该技能指导智能体连接 CodeScene MCP 服务器,并调用四个独立工具:code_health_review、code_health_score、pre_commit_code_health_safeguard 和 analyze_change_set。它定义了修改前审查、修改后验证的循环,解释 1-10 分的评分,并规定服务器不可用时的行为。它产出结构性可维护性反馈和回归门禁,而非代码改动。
适用场景
适用于审查代码质量、重构文件、检查 AI 改动是否降低可维护性,或在提交和拉取请求之前。它被定位为与 lint、测试和验证流程并用的结构性检查。
运行要求
需要在智能体运行环境中配置 CodeScene MCP 服务器(@codescene/codehealth-mcp,通过 npx 运行),并设置 CS_ACCESS_TOKEN 环境变量。需要网络访问以安装和运行该服务器,并会读取本地仓库文件和 git 状态。不附带脚本。

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):

json
"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

ToolWhen to use
code_health_reviewFull structural analysis before modifying a file
code_health_scoreQuick numeric score after each change (delta check)
pre_commit_code_health_safeguardBlock commits that introduce Code Health regressions
analyze_change_setBranch-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)

RangeMeaningAgent behavior
9.0–10.0Green — healthySafer to extend; still prefer vertical slices
4.0–8.9Yellow — debtTread carefully; no drive-by refactors
1.0–3.9Red — severe debtNarrow scope only

4. Run the feedback loop

Before touching a file

  1. Run code_health_review on the target path.
  2. Record baseline score and listed code smells.
  3. 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

  1. Run code_health_score on the same file.
  2. Compare to the baseline from code_health_review.
  3. 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:

  1. code_health_review on a target module (baseline 4.82)
  2. Targeted refactor addressing listed smells
  3. code_health_score after each edit
  4. pre_commit_code_health_safeguard before commit
  5. 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:

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

markdown
# 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 / flowCode Health MCP role
coding-standardsStyle/naming; Code Health = structure/complexity
plankton-code-qualityWrite-time lint/format; Code Health = pre/post edit structural gate
verification-loop / /quality-gateAdd structural regression check before "done"
security-reviewSecurity vs maintainability — use both when relevant
tdd-workflowTests 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)

来源与署名

来源:affaan-m/ecc位于skills/codehealth-mcp提交ef648e0

许可证: 无许可证

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