Atlas / Skills / caliber-ai-org / Scoring Checks

Scoring ChecksSAFE

skills/caliber-ai-org/scoring-checks

Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
4 documented
License
MIT
Stars
1,302
01

Overview

Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.

Read from source at commit b38062d63a05OBSERVED · 2026-10-10
02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
codexmentioned
copilotmentioned
cursormentioned
03

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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.
---
# 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):

```typescript
// 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:

```typescript
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`:

```typescript
// 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:

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

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

```typescript
export function computeLocalScore(dir: string, targetAgent?: TargetAgent): ScoreResult {
  const target = targetAgent ?? detectTargetAgent(dir);

  const allChe
04

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-10 · audit v0.4.1 · source sha b38062d63a05full audit observations/trust-audit/skill/caliber-ai-org__scoring-checks.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-10b38062d63a05SAFEB89first audit
06

Questions

What does the Scoring Checks skill do?

Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex.

Is Scoring Checks safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Scoring Checks access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Scoring Checks work with?

Its documentation mentions claude-code, codex, copilot and cursor. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (b38062d63a05), read on 2026-10-10. The repository is watched, and a new audit runs when it changes — this is the first audit.

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