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.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/eval-creator/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/eval-creator)<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>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.
| Model | Per session | Once 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 |
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.
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
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.
- 8d ago First seen · 271 lines · 67 tokens per session scan A 1509762921f3
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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