/scoring-checks

name: scoring-checks
description: Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality. Follows the Check[] return pattern, uses point constants from src/scoring/constants.ts, and integrates via filterChecksForTarget() in src/scoring/index.ts. Use when user says 'add scoring check', 'new check', 'modify scoring criteria', or works in src/scoring/checks/. Do NOT use for display changes or refactoring scoring logic.
paths:
  - src/scoring/checks/**/*.ts
  - src/scoring/constants.ts
  - src/scoring/index.ts

Adding a Scoring Check

Add a new deterministic check that evaluates a single aspect of AI agent config quality. All checks must be filesystem-based with no network calls or LLM inference.

Critical

• **Check must be deterministic**: Same filesystem state → same result every time. No randomness, no external APIs.
• **Point values come from constants.ts**: Every `earnedPoints` and `maxPoints` must reference `POINTS_*` from `src/scoring/constants.ts`. Do NOT hardcode numbers.
• **Always return `Check[]` array**: Export a function `check<Category>(dir: string): Check[]` where category is one of: `existence`, `quality`, `grounding`, `accuracy`, `freshness`, `bonus`.
• **Every check must have**: `id` (kebab-case, unique), `name`, `category`, `maxPoints`, `earnedPoints`, `passed`, `detail`, and optional `suggestion`/`fix`.
• **Fix object fields**: `action` (string describing what to do), `data` (context for the fix), `instruction` (user-facing guidance).
• **Register in src/scoring/index.ts**: Add the import and spread the result into the `allChecks` array in `computeLocalScore()`.
• **Target filtering**: If the check is platform-specific (Claude-only, Cursor-only, etc.), add its ID to the appropriate `*_ONLY_CHECKS` set in `constants.ts`.

Instructions

Step 1: Define point constants in src/scoring/constants.ts

Verify before proceeding: Is your check measurable with a numeric point value?

Add constants below the appropriate category section (existence, quality, grounding, accuracy, freshness, bonus):

// In the appropriate CATEGORY section, e.g., Quality checks (25 pts):
export const POINTS_YOUR_CHECK_NAME = 4; // 1-12 pts typical

// If threshold-based, add a companion array:
export const YOUR_THRESHOLD_ARRAY = [
  { minValue: 10, points: 4 },
  { minValue: 5, points: 2 },
] as const;

Check existing patterns: Token budgets use `TOKEN_BUDGET_THRESHOLDS`, code blocks use `CODE_BLOCK_THRESHOLDS`, concreteness uses `CONCRETENESS_THRESHOLDS`.

Verify: Review `CATEGORY_MAX` object to ensure your check fits within its category's point budget.

Step 2: Create or edit check function in src/scoring/checks/

Choose the file based on category. Each file exports a `check<Name>(dir: string): Check[]` function:

• `existence.ts` — files/directories exist (CLAUDE.md, .cursorrules, skills, MCP servers)
• `quality.ts` — config structure, size, clarity (code blocks, token budget, concreteness, duplicates)
• `grounding.ts` — references to actual project files/directory structure
• `accuracy.ts` — validity of references, git-based config drift
• `freshness.ts` — git commit-based staleness, secrets, permissions
• `bonus.ts` — hooks, learned content, OpenSkills format
• `sources.ts` — source configuration and usage

Create the function following this structure:

import type { Check } from '../index.js';
import {
  POINTS_YOUR_CHECK,
  YOUR_THRESHOLD_ARRAY,
} from '../constants.js';
import { readFileOrNull } from '../utils.js'; // or other helpers

export function checkYourCategory(dir: string): Check[] {
  const checks: Check[] = [];

  // 1. Measure something concrete
  const yourMetric = /* e.g., countFiles(), validatePaths(), etc. */;
  const threshold = YOUR_THRESHOLD_ARRAY.find(t => yourMetric >= t.minValue);
  const earnedPts = threshold?.points ?? 0;

  checks.push({
    id: 'your_unique_check_id',
    name: 'Human-readable check name',
    category: 'quality', // matches function context
    maxPoints: POINTS_YOUR_CHECK,
    earnedPoints: earnedPts,
    passed: earnedPts >= Math.ceil(POINTS_YOUR_CHECK * 0.6), // or custom logic
    detail: `${earnedPts}/${POINTS_YOUR_CHECK} points — ${yourMetric} items found`,
    suggestion: earnedPts >= POINTS_YOUR_CHECK ? undefined : 'Action to improve',
    fix: earnedPts >= POINTS_YOUR_CHECK ? undefined : {
      action: 'verb_noun', // e.g., 'add_code_blocks', 'fix_references'
      data: { currentValue: yourMetric, targetValue: 10 },
      instruction: 'Specific, actionable guidance for the user.',
    },
  });

  return checks;
}

Verify ID uniqueness: Run `grep -r "'your_unique_check_id'" src/scoring/checks/` — should return only your new check.

Step 3: Handle platform-specific filtering (if applicable)

If your check only applies to certain agents (Claude, Cursor, Codex, GitHub Copilot), register it in `src/scoring/constants.ts`:

// Add to the appropriate set:
export const CLAUDE_ONLY_CHECKS = new Set([
  'claude_md_exists',
  'your_new_check_id', // ← add here
  'claude_rules_exist',
]);

Available sets (update exactly one if applicable):

• `CLAUDE_ONLY_CHECKS` — Claude Code targets
• `CURSOR_ONLY_CHECKS` — Cursor targets
• `CODEX_ONLY_CHECKS` — Codex/OpenCode targets
• `COPILOT_ONLY_CHECKS` — GitHub Copilot targets
• `BOTH_ONLY_CHECKS` — Both Claude AND Cursor (cross-platform parity)
• `NON_CODEX_CHECKS` — Everything except Codex/OpenCode
• `CLAUDE_OR_CODEX_CHECKS` — Claude OR Codex

Verify filtering: Examine `filterChecksForTarget()` in `src/scoring/index.ts` to ensure your category will work correctly for your target agents.

Step 4: Register in src/scoring/index.ts

Import your function at the top:

import { checkYourCategory } from './checks/your-file.js';

Add to `computeLocalScore()` inside the `allChecks` array initialization:

export function computeLocalScore(dir: string, targetAgent?: TargetA