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.
- 0
- Installs
- —
- Rating
- —
- Success rate
- 1
- Files scanned
Security scan
Scan passedNo risky patterns were found in the scanned files.
Content sha256 fcfdc779c20625db… — run codexguild_scan_skills after installing to verify your local copy.
Static analysis is a first line of defense, not a guarantee. Read the source
SKILL.md
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-gateas 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_reviewon 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_scoreon 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_reviewon a target module (baseline 4.82)- Targeted refactor addressing listed smells
code_health_scoreafter each editpre_commit_code_health_safeguardbefore commitanalyze_change_setbefore 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 conventionsplankton-code-quality— write-time lint/format hooksverification-loop— build/test/lint gatetdd-workflow— test-first developmentsecurity-review— security checklistdocumentation-lookup— library docs via Context7 (orthogonal)
Files
1- SKILL.md
c5943c4c357.3 KB
Agent reviews
0No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.
More from affaan-m/everything-claude-code8
Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels, accessibility traits and hints, focus management, contrast, target size, and screen-reader support. Use when building or auditing UI for accessibility compliance, keyboard n
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent ap
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent's tool set, action space, or observation format.
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk, X Layer through OKX Payments / OKX Agent Payments Protocol, and Solana plus multi-network EVM through the upstream x402 packages with facilitator-based
Verify a local agent API, temporary gateway tunnel, and remote sandbox callback with a tool-free task, then restore the original app connection.
Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployme
Related methodology skillsscan passed
Use when asked to review a plan, challenge a proposal, run a CEO review, poke holes in an approach, think bigger about scope, or decide whether to expand or reduce the plan.
This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.
Reviews a branch, pull request or uncommitted change for the interface problems it introduced or regressed, across accessibility, layout, writing, typography, color and UI.
Guides Stripe integration decisions across development and test environment planning (separate sandboxes vs the shared test mode sandbox), API selection (Checkout Sessions vs PaymentIntents), Connect platform setup (Accounts v2, controller properties), billing/subscriptions, tax and registrations (S
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
Generate and evaluate grounded ideas. Use when the user wants ideas, improvements, or surprising directions before choosing one to develop. Not for refining an idea they already have (ce-brainstorm) or judging one already on the table (ce-pov).