Borrowing it
Nothing to install: this file belongs to abuango/pos-ai. 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/abuango/pos-ai/main/.claude/commands/eval-loop.mdgit clone --depth 1 https://github.com/abuango/pos-aiWrote 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/commands/abuango/pos-ai/eval-loop)<a href="https://agentmods.dev/commands/abuango/pos-ai/eval-loop"><img src="https://agentmods.dev/badge/commands/abuango/pos-ai/eval-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/abuango/pos-ai/eval-loop"><img src="https://agentmods.dev/badge/commands/abuango/pos-ai/eval-loop.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00643 |
| Opus 5.5 | $0.00000 | $0.00257 |
| Sonnet 5.5 | $0.00000 | $0.00129 |
| Haiku 4.5 | $0.00000 | $0.00064 |
Grade A, and why
eval-loop 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 2mo 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running an evaluator-optimizer loop on the code that was just written or modified in this session. This implements a structured generate-evaluate-refine cycle with a maximum of 3 iterations.
Process
Step 1: Capture What Changed
Run git diff --unified=3 to see all unstaged changes, or git diff --cached for staged changes. If no diff exists, ask the user which files to evaluate.
Step 2: Evaluate Against Criteria
For each changed file, check these criteria:
Code Quality (from .claude/rules/code-quality.md):
- No functions longer than 30 lines
- No logic duplicated more than twice
- No
anytype usage in TypeScript - Components with 3+ related props use object grouping
- All async operations have error handling
- No
SELECT *queries - Frequently queried columns have indexes
Security (critical — auto-fail):
- No hardcoded secrets or credentials
- No SQL injection vectors (raw queries with interpolation)
- No XSS vectors (innerHTML, v-html, dangerouslySetInnerHTML)
- No command injection (exec/system with user input)
- Input validation present at system boundaries
Correctness:
- Logic matches the approved plan intent
- Edge cases handled (null, empty, boundary values)
- No off-by-one errors in loops/pagination
- Error messages are actionable
Step 3: Report Findings
For each issue found, report:
[FAIL] {criteria} — {file}:{line}
Issue: {what's wrong}
Fix: {specific fix}
If all criteria pass:
[PASS] All criteria met. No issues found.
Step 4: Fix and Re-evaluate (if needed)
If issues were found:
- Fix each issue using the Edit tool
- Re-run the evaluation (Step 2) on the fixed files only
- Report iteration results
Iteration tracking:
--- Iteration {n}/3 ---
Issues found: {count}
Issues fixed: {count}
Remaining: {count}
Step 5: Final Report
After all iterations (or when all criteria pass), output:
## Eval-Loop Complete
Iterations: {n}/3
Starting issues: {count}
Resolved: {count}
Remaining: {count}
Status: PASSED / PARTIAL (N issues remain) / FAILED (critical issues)
{List any remaining issues if not fully resolved}
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.
- 2mo ago First seen · 87 lines · 0 tokens per session scan A f97e9c741b4c
eval-loop is a command published in the GitHub repository abuango/pos-ai (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 643 tokens. 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.
Other commands, from other repositories
review-branch
Review the current branch's diff against base by dispatching atomic-reviewer. No orchestration loop, no spec required — pre-flight before /commit pr or /commit merge.
refactor
Systematic code refactoring with smart scope detection.
init
Install the formatters this repository needs, with every command visible before it runs.
spec-trace-requirements
You are verifying that implementation changes trace back to specification requirements.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).