Agent skill

Scoring Checks

by caliber-ai-org in caliber-ai-org/ai-setup

Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality.

MITAuto-check passedDevelopment

Install Scoring Checks

skills CLI
$ npx skills add caliber-ai-org/ai-setup --skill scoring-checks -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install caliber-ai-org/ai-setup scoring-checks --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/caliber-ai-org/ai-setup.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/scoring-checks .claude/skills/scoring-checks && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scoring-checks
GitHub stars
1.3k
Token cost
~3k tokens
SKILL.md length
908 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality.

  • Works in 5 steps: Define point constants in… → Create or edit check function in… → Handle platform-specific filtering (if… → …
  • User says add scoring check
  • SKILL.md covers Critical, Instructions, Examples and Common Issues
  • Calls npm

What it does

Scoring Checks is an agent skill from caliber-ai-org/ai-setup. 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.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Refactoring. It works with Model Context Protocol. The repository describes itself as: Continuously sync your AI setups with one command. Codebase tailor suited agent skills, MCPs and config files for Claude Code, Cursor, and Codex. The licence is MIT.

When your agent uses it

  • User says add scoring check
  • Modify scoring criteria
  • Works in src/scoring/checks/
  • Display changes

Example prompts

  • “add scoring check”
  • “new check”
  • “modify scoring criteria”
  • “/scoring-checks”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Define point constants in src/scoring/constants.ts
  2. Create or edit check function in src/scoring/checks/
  3. Handle platform-specific filtering (if applicable)
  4. Register in src/scoring/index.ts
  5. Write deterministic unit tests

What it can do on your machine

Read from SKILL.md and the folder at commit f5dbc00. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scoring Checks loads about 3k tokens when it runs. Until then it costs about 109 tokens; SKILL.md has 908 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from caliber-ai-org/ai-setup at commit f5dbc00, republished under its MIT licence (© caliber-ai-org). 908 words, ~3,017 tokens.

Download SKILL.mdSave it as .claude/skills/scoring-checks/SKILL.md (or your agent's skills folder).
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):

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 allChecks: Check[] = [
    ...checkExistence(dir),
    ...checkQuality(dir),
    ...checkGrounding(dir),
    ...checkAccuracy(dir),
    ...checkYourCategory(dir), // ← ADD HERE IN ORDER
    ...checkFreshness(dir),
    ...checkBonus(dir),
    ...checkSources(dir),
  ];
  // ... rest of function
}

Verify registration: Run npm test src/scoring/__tests__/accuracy.test.ts (or similar) — all existing tests should still pass.

Step 5: Write deterministic unit tests

Create or edit src/scoring/checks/__tests__/your-file.test.ts:

typescript
import { describe, it, expect } from 'vitest';
import { mkdtempSync, writeFileSync, rmSync } from 'fs';
import { join } from 'path';
import { checkYourCategory } from '../your-file.js';
import { POINTS_YOUR_CHECK } from '../../constants.js';

describe('checkYourCategory', () => {
  it('awards full points when condition passes', () => {
    const dir = mkdtempSync('test-scoring-');
    try {
      // Set up the passing condition
      writeFileSync(join(dir, 'SOME_FILE.md'), 'content that satisfies check');
      
      const checks = checkYourCategory(dir);
      const check = checks.find(c => c.id === 'your_unique_check_id');
      
      expect(check).toBeDefined();
      expect(check?.passed).toBe(true);
      expect(check?.earnedPoints).toBe(POINTS_YOUR_CHECK);
    } finally {
      rmSync(dir, { recursive: true });
    }
  });

  it('awards zero points when condition fails', () => {
    const dir = mkdtempSync('test-scoring-');
    try {
      // Don't create the required condition
      const checks = checkYourCategory(dir);
      const check = checks.find(c => c.id === 'your_unique_check_id');
      
      expect(check?.passed).toBe(false);
      expect(check?.earnedPoints).toBe(0);
    } finally {
      rmSync(dir, { recursive: true });
    }
  });

  it('returns correct detail message', () => {
    const dir = mkdtempSync('test-scoring-');
    try {
      const checks = checkYourCategory(dir);
      const check = checks.find(c => c.id === 'your_unique_check_id');
      expect(check?.detail).toBeTruthy();
    } finally {
      rmSync(dir, { recursive: true });
    }
  });
});

Run tests: npm test src/scoring/checks/__tests__/your-file.test.ts. All must pass before shipping.

Examples

Example 1: Existence Check

Trigger: User says "Add a check to verify .claude/rules/ directory exists."

Actions:

  1. Add export const POINTS_CLAUDE_RULES = 3; to constants.ts
  2. In existence.ts: existsSync(join(dir, '.claude', 'rules')) → true/false
  3. Import in index.ts and add ...checkExistence(dir) (already done)
  4. Test with mkdtempSync; verify earnedPoints matches POINTS_CLAUDE_RULES

Result: Check id: 'claude_rules_exist' returns earnedPoints: 3, passed: true when dir exists.

Example 2: Quality Check with Thresholds

Trigger: User says "Verify config has at least 3 code blocks with executable commands."

Actions:

  1. Add to constants.ts:
    typescript
    export const CODE_BLOCK_THRESHOLDS = [
      { minBlocks: 3, points: 8 },
      { minBlocks: 2, points: 6 },
      { minBlocks: 1, points: 3 },
    ] as const;
  2. In quality.ts:
    • Parse CLAUDE.md with regex to count ``` blocks
    • Match against CODE_BLOCK_THRESHOLDS
    • Return points based on threshold match
  3. In fix: Suggest which commands to add

Result: 3+ blocks = 8 pts, 2 blocks = 6 pts, 1 block = 3 pts, 0 blocks = 0 pts.

Show full SKILL.md (338 more words)Show less
Example 3: Accuracy Check (Reference Validation)

Trigger: User says "Check that all file paths mentioned in config actually exist."

Actions:

  1. Add export const POINTS_REFERENCES_VALID = 8; to constants.ts
  2. In accuracy.ts:
    • Extract backtick-quoted paths and dir patterns from config
    • Check existence with existsSync(join(dir, path))
    • Calculate ratio: valid / total
    • Award partial points: Math.round(ratio * POINTS_REFERENCES_VALID)
  3. In fix: List invalid paths the user should fix

Result: 80% valid refs = ~6 pts; 100% valid = 8 pts; 0% valid = 0 pts.

Common Issues

Issue: "My check doesn't appear in the score report." Fix: 1) Verify ID in *_ONLY_CHECKS if platform-specific. 2) Verify import and spread in index.ts allChecks array. 3) Run npm test to ensure no tsc errors. 4) Check detectTargetAgent() returns your target platform.

Issue: "Points are hardcoded but should use constants." Fix: Replace all literal numbers like earnedPoints: 5 with earnedPoints: POINTS_YOUR_CHECK. Constants are in src/scoring/constants.ts — use them consistently.

Issue: "Check makes an API call or network request." Fix: Scoring MUST be deterministic and offline. Use only: fs module (readFileSync, existsSync, readdirSync), path, execSync for git commands. No HTTP, no LLM calls, no external services.

Issue: "Platform-specific check appears for the wrong agent." Fix: 1) Verify check ID is in correct *_ONLY_CHECKS set. 2) Double-check filterChecksForTarget() handles your platform set. 3) Test with detectTargetAgent() on a real project.

Issue: "Test fails with 'Module not found' error." Fix: Ensure file is in src/scoring/checks/ (not nested). Use .js extension in imports (TypeScript transpiles to ES modules). Run npm run build to check for tsc errors.

Issue: "Detail message is confusing or too technical." Fix: Use friendly language: 3 code blocks found (need 3 for full points) instead of codeBlockCount=3. Make it clear WHY they got/lost points.

Issue: "Threshold-based check gives wrong points for edge cases." Fix: Test all boundaries: value=0, value=threshold, value>>threshold. Use .find() to match highest-to-lowest: find(t => value >= t.minValue).

Issue: "Two checks have the same ID." Fix: Run grep -r "'my_id'" src/scoring/checks/ to find duplicates. IDs must be globally unique across all check files. Use descriptive names like claude_md_exists, not check_1.

© caliber-ai-org, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/scoring-checks of caliber-ai-org/ai-setup.

Open the folder on GitHubat commit f5dbc00

Compare with similar skills

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Questions about Scoring Checks

What does Scoring Checks do?

Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality. Scoring Checks is an agent skill from caliber-ai-org/ai-setup. Add a new deterministic scoring check in src/scoring/checks/ that evaluates config quality.

When should I use Scoring Checks?

Scoring Checks fits situations like: user says add scoring check; modify scoring criteria; works in src/scoring/checks/; display changes.

How do I install Scoring Checks in Claude Code?

Run `npx skills add caliber-ai-org/ai-setup --skill scoring-checks -a claude-code`. Or copy the skill folder (skills/scoring-checks in caliber-ai-org/ai-setup) into .claude/skills/scoring-checks in your project. Claude Code loads it when a task matches its description.

How do I install Scoring Checks in Codex?

Run `npx skills add caliber-ai-org/ai-setup --skill scoring-checks -a codex`. Or copy the skill folder (skills/scoring-checks in caliber-ai-org/ai-setup) into .agents/skills/scoring-checks in your project. Codex loads it when a task matches its description.

Can I use Scoring Checks in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add caliber-ai-org/ai-setup --skill scoring-checks -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scoring-checks, .gemini/skills/scoring-checks, .github/skills/scoring-checks and .opencode/skills/scoring-checks in your project.

What does Scoring Checks need to run?

Going by SKILL.md and its folder, Scoring Checks needs the command-line tools its instructions call (npm).

Does Scoring Checks access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Scoring Checks safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scoring Checks use?

Scoring Checks is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scoring Checks use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scoring Checks?

Skills that share tags, products or a category with Scoring Checks: Filescope MCP (admica/FileScopeMCP, 302 stars), Gograph Go Repository Intelligence (ozgurcd/gograph, 229 stars), Memtrace Decision Memory (syncable-dev/memtrace-public, 489 stars) and Testing With API Mocks (stacklok/toolhive-studio, 171 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scoring Checks?

caliber-ai-org (a GitHub organization) maintains it in caliber-ai-org/ai-setup, which has 1,302 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 24, 2026.

Source: caliber-ai-org/ai-setup on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.