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Codehealth Mcp

  • 1.1k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/everything-claude-code

codehealth-mcp is a Claude Code skill that wraps the CodeScene Code Health MCP to score structural maintainability before edits, verify deltas after changes, and gate commits and PRs.

About

codehealth-mcp is a Claude Code skill that drives the CodeScene Code Health MCP server to give structural, design-level maintainability feedback for AI-assisted coding. It reviews a file before edits, checks the score delta after changes, and gates commits and pull requests against regressions. A developer uses it when reviewing code quality, refactoring, or checking whether an AI diff degraded a file. It complements lint and style skills with design-level health scores rather than replacing tests.

  • Wraps the CodeScene Code Health MCP for structural maintainability scores
  • Review before edits, verify score deltas after changes, gate commits and PRs
  • Uses four standalone tools and a 1-10 score scale to scope how aggressively to change a file

Codehealth Mcp by the numbers

  • 1,066 all-time installs (skills.sh)
  • +218 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #113 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
At a glance

codehealth-mcp capabilities & compatibility

Requires a CS_ACCESS_TOKEN; standalone mode's four tools do not require a paid CodeScene platform account.

Capabilities
code review · maintainability scoring · regression gating · refactoring
Works with
github
Use cases
code review · refactoring
From the docs

What codehealth-mcp says it does

Real-time structural Code Health via CodeScene MCP — review before edits, verify score deltas after changes, gate commits and PRs.
SKILL.md
Never mark the task done while the score is lower than when you started.
SKILL.md
npx skills add https://github.com/affaan-m/everything-claude-code --skill codehealth-mcp

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Listed on Skillselion
Installs1.1k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/everything-claude-code

What it does

Score a file's structural Code Health before and after edits and gate commits or PRs against maintainability regressions.

Who is it for?

Reviewing code quality, refactoring, checking whether AI changes degraded a file, and gating commits or PRs.

Skip if: Analyzing secrets, credentials, or paths you do not want analyzed, and it is not a replacement for tests and lint.

When should I use this skill?

The user asks to review code quality or refactor, before editing a hotspot, or before a commit or PR needing a maintainability safeguard.

What you get

A recorded baseline and post-change Code Health score that blocks commits and PRs when maintainability regresses.

By the numbers

  • 4 standalone tools
  • 1-10 Code Health score scale
  • 3 score ranges (green/yellow/red)

Files

SKILL.mdMarkdownGitHub ↗

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.jsonmcpServers):

"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:

## 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 / 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)

Related skills

FAQ

What credentials does it need?

No bundled token; you set CS_ACCESS_TOKEN yourself, and standalone mode does not require a paid CodeScene platform account for the four tools.

What happens if the MCP is unavailable?

Do not invent scores; tell the user the check was skipped and prefer lint, tests, and the verification loop for gating.

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