measuring-ai-proficiency: Skill for Claude Code

.claude/skills/eval-creator/SKILL.md

eval-creator is a skill for Claude Code from pskoett/measuring-ai-proficiency. It costs 67 tokens per session (2,167 once invoked), scanned A, original, MIT.

A skill for turning important lessons from development work into permanent evaluation cases. An evaluation case is a repeatable test that checks whether a rule or behavior still works.

In plain words
What is it for?
Use it after promoting a lesson into project instructions, before major releases, or when checking whether previously fixed behavior has regressed.
Why use it?
It helps prevent known mistakes from returning by recording them as tests and running regression checks over time.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths; mentions CLAUDE.md; mentions AGENTS.md.

This is pskoett/measuring-ai-proficiency's own configuration. It tells Claude Code how to work on measuring-ai-proficiency itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything measuring-ai-proficiency configures →

Reuse

Borrowing it

Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/eval-creator/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/pskoett/measuring-ai-proficiency

Made for: Claude Code.

Wrote this? Show the measurements

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agentmods badge for eval-creator

README.md
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<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/eval-creator"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/eval-creator.svg" alt="Measured on agentmods" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,167 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.02167
Opus 5 $0.00034 $0.01084
Sonnet 5 $0.00013 $0.00433
Haiku 4.5 $0.00007 $0.00217

Measured 8d ago against content hash 1509762921f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

eval-creator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/eval-creator/SKILL.md · 271 lines

How it starts

The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Eval Creator

Turns promoted learnings into permanent eval cases. Runs regression checks to verify promoted rules hold. This is the outer loop's regress-test step.

The blog says: "If a failure taught you something important, it should become a permanent test case. Otherwise the knowledge is still fragile."

When to Use

  • After harness-updater promotes a pattern — create an eval for it
  • On cadence — run all evals to check for regression
  • Before major releases — verify the harness is holding
  • When a promoted rule seems to have stopped working — diagnose with targeted eval run

Eval Directory Structure

.evals/
  EVAL_INDEX.md          # Index of all eval cases with status
  cases/
    eval-YYYYMMDD-001.md # Individual eval case
    eval-YYYYMMDD-002.md
    ...

Creating an Eval Case

Input

From harness-updater or manually:

  • Pattern-Key of the promoted learning
  • The rule that was added to the project instruction files (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md)
  • What to test (the assertion)
  • Verification method

Eval Case Format

---
id: eval-YYYYMMDD-NNN
pattern-key: [from learning]
source: [LRN-YYYYMMDD-001, ERR-YYYYMMDD-003]
promoted-rule: "[the rule text in project instruction files]"
promoted-to: CLAUDE.md  # or AGENTS.md, .github/copilot-instructions.md, or equivalent
created: YYYY-MM-DD
last-run: YYYY-MM-DD
last-result: pass | fail | skip
---

## What This Tests

[One sentence: what failure this eval prevents from recurring]

## Precondition

[What must be true for this eval to be runnable]
- File X exists
- Project uses framework Y
- etc.

## Verification Method

[One of: grep-check, command-check, file-check, rule-check]

### grep-check
Search for a pattern that should (or should not) exist:

target: src/**/*.ts pattern: "hardcoded-secret-pattern" expect: not_found


### command-check
Run a command and check the exit code or output:

command: npm run typecheck expect_exit: 0


### file-check
Verify a file or section exists:

target: CLAUDE.md # or AGENTS.md, .github/copilot-instructions.md section: "## Verification" expect: exists


### rule-check
Verify a rule exists in an instruction file:

target: CLAUDE.md # or AGENTS.md, .github/copilot-instructions.md contains: "[the promoted rule text or key phrase]" expect: found


## Expected Result

**Pass:** [What "good" looks like]
**Fail:** [What regression looks like]

## Recovery Action

If this eval fails:
1. [Specific step to diagnose]
2. [Specific step to fix]
3. Re-run this eval to verify

Read the full file on GitHub · 271 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 271 lines · 67 tokens per session scan A 1509762921f3

Subscribe to this mod's changes

eval-creator is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 2,167 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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