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Scoring Checks

OrganizationPopular
caliber-ai-org
scoring-checks

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.

Overview

Publishercaliber-ai-org
Repositoryai-setup
Skill namescoring-checks
Stars
1.3K
Forks
124
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by caliber-ai-org on GitHub. Read the source before you install it.

Installation

Install the Scoring Checks AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/caliber-ai-org/ai-setup.git /tmp/ai-setup
mkdir -p .claude/skills
cp -r /tmp/ai-setup/skills/scoring-checks .claude/skills/scoring-checks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scoring Checks in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scoring Checks on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scoring Checks is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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.

Frequently asked questions

What does the Scoring Checks AI skill do?

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.

Why use Scoring Checks on TypingMind?

Because you install it once and use it with any model. Scoring Checks is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scoring Checks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/caliber-ai-org/ai-setup/tree/master/skills/scoring-checks. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scoring Checks?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scoring Checks?

As many as you like. As long as a model supports skills, you can use Scoring Checks with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scoring Checks AI skill free?

Yes. It is published on GitHub by caliber-ai-org under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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