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.
npx skills add caliber-ai-org/ai-setup --skill scoring-checks --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 t
- Critical
- Instructions
- Step 1: Define point constants in src/scoring/constants.ts
- Step 2: Create or edit check function in src/scoring/checks/
- Step 3: Handle platform-specific filtering (if applicable)
- Step 4: Register in src/scoring/index.ts
- Step 5: Write deterministic unit tests
- Examples
- Example 1: Existence Check
- Example 2: Quality Check with Thresholds
- Example 3: Accuracy Check (Reference Validation)
- Common Issues
What does the scoring-checks 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.
How do I install it?
Run `npx skills add caliber-ai-org/ai-setup --skill scoring-checks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From caliber-ai-org/ai-setup, a repository with 1,235 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.