Skill Rubrics for AI Coding Teams

A team with mixed coding skill should standardize acceptance criteria before it standardizes the agent. For AI coding for teams, the useful unit is a small convention that says which work can be delegated, what evidence must come back, and who owns the merge.

I would treat AI code solutions for diverse coding skills teams as workflow design, not tool shopping. Coding agents can raise developer productivity, but only when the team makes skill, risk, and review visible enough for ordinary pull request discipline.

Start from the team’s skill spread

Mixed skill is normal. One engineer may know the domain model deeply but be new to the test framework. Another may be strong in TypeScript and weak in database migrations.

The agent should not erase those differences. It should make the handoff sharper: what may be delegated, what must be checked, and where a senior reviewer must slow the work down.

A concrete workflow: in a TypeScript service that uses Prisma, a guided developer can ask an agent to draft a repository method, a migration, and Jest tests. The pull request still needs the migration diff, the test output, and a reviewer who understands the data model before merge.

Use a skill acceptance rubric

Put the convention in the repo where engineers already look for working agreements. That may be docs/ai-coding.md, a team handbook, or an agent instruction file if your tool supports repository rules.

Paste this first version, then edit the risk bands for your stack.

# AI coding skill acceptance rubric

Purpose: Give coding agents clear limits for mixed-skill team work. The human author owns the change, even when the agent wrote most of the patch.

## Skill bands

## Supported
Use for engineers who are new to the codebase, language, framework, or domain area.

Allowed agent work:
- Explain existing code paths.
- Draft small tests from an existing pattern.
- Suggest a plan for a change under 100 lines.
- Generate throwaway scripts that do not touch production data.

Required evidence before pull request:
- Human-written summary of the intended change.
- Link to the issue or task.
- Test command and result.
- Reviewer assigned before implementation begins.

Blocked without pairing:
- Database schema changes.
- Authentication or authorization changes.
- Payment, billing, security, or privacy-sensitive code.
- Agent access to production systems.

## Guided
Use for engineers who can ship ordinary changes in this repo but need review for risky areas.

Allowed agent work:
- Draft implementation patches for scoped tickets.
- Add tests that follow existing project style.
- Refactor within one module when public behavior is unchanged.
- Prepare migration drafts for reviewer inspection.

Required evidence before merge:
- Before and after behavior summary.
- Test output, lint output, or a clear reason the command was not run.
- Files the agent touched intentionally listed in the PR description.
- Reviewer check on risk areas named in this rubric.

Blocked without senior review:
- Cross-service changes.
- New external dependencies.
- Changes to permissions, secrets, data retention, or audit logging.
- Large generated patches that the author cannot explain.

## Independent
Use for engineers who already own this area of the system.

Allowed agent work:
- Implement scoped features.
- Draft refactors with tests.
- Generate review checklists for complex pull requests.
- Use approved MCP-connected tools for read-only context gathering.

Required evidence before merge:
- Clear PR description.
- Tests or verification steps.
- Notes on agent-generated code that was rejected or rewritten.
- Reviewer chosen for the riskiest part of the change, not just team availability.

Blocked without explicit approval:
- Write access through MCP to issue trackers, repositories, databases, or deployment systems.
- Automated merge decisions.
- Silent changes to generated code, lockfiles, migrations, or security policy.

## Team review rule

A pull request may use agent-generated code only when the author can explain the changed behavior, name the files the agent touched, and show the verification used. If any of those are missing, the reviewer asks for a smaller change or a pairing session.

Adopt it where work already happens

One tech lead or senior engineer should propose the first rubric. Do not run this as a committee exercise at the start, because the first version needs to be concrete enough to argue with.

Two reviewers should edit it before adoption: one person who reviews a lot of pull requests, and one person who represents the least experienced regular user of the agent workflow. That second review matters because a rubric that only senior engineers understand will quietly fail.

Store the convention beside existing engineering rules. If your team uses repository instructions for coding agents, link to the rubric there, but keep the human-readable version in the repo as well.

This is the Review step in our methodology: delegate the draft, then require evidence before ownership transfers back to the team.

Keep the review rule enforceable

The review rule should be boring enough to apply on every pull request. The author must explain the behavior, name the files the agent touched, and show verification.

That rule catches the common failure mode: a large agent patch that passes a quick glance but cannot be explained by the person opening the pull request. When that happens, the fix is a smaller patch, a pairing session, or a rollback to planning.

Use code review guardrails before you add more automation. Branch protection, required tests, and reviewer ownership still do more work than a clever prompt.

Set limits for tools that reach outside the repo

MCP is now a common way to connect coding agents to systems such as GitHub, issue trackers, document stores, and databases. That can be useful, but the permission boundary changes once the agent can call external tools.

Start read-only. Let the agent inspect issues, docs, or code search before it can write comments, create branches, update tickets, or touch data. For teams experimenting with structured agent judgments, the pattern in typesafe-mcp Adds Typed Agent Judgments is worth studying because it treats agent output as something to validate, not just something to trust.

There is a tradeoff. Stricter limits slow the first week of adoption, especially for senior engineers who want speed. The payoff is that junior and senior developers can use the same agentic coding workflow without pretending they carry the same risk.

Roll it out in five steps

Use this sequence when the team is already using coding agents in real work.

1. Pick one repo with active pull requests and normal CI.
2. Add the rubric to the repo and link it from the team’s agent instructions.
3. Choose one risk area, such as migrations or authentication, and make the blocked rule explicit.
4. Ask reviewers to enforce only the evidence rule for the first week.
5. After five to ten pull requests, revise the bands based on where review actually slowed down.

This is small enough for an AI coding workshop or internal engineering team training session. The point is not to certify people. The point is to make delegation safe enough that different skill levels can contribute without hiding uncertainty.

Further reading

• Cursor Agent docs
• Claude Code docs
• Codex quickstart
• MCP specification
• OWASP Top 10 for LLM Applications

Put this into one repo this week

Choose one active repository and add the rubric before the next agent-assisted pull request. Review the first few PRs against the evidence rule, then tighten only the parts that reviewers actually use.