Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/seokan-jeong/team-shinchanWrote 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/seokan-jeong/team-shinchan/eval)<a href="https://agentmods.dev/commands/seokan-jeong/team-shinchan/eval"><img src="https://agentmods.dev/badge/commands/seokan-jeong/team-shinchan/eval/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/seokan-jeong/team-shinchan/eval"><img src="https://agentmods.dev/badge/commands/seokan-jeong/team-shinchan/eval.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 | $0.00007 | $0.00084 |
| Opus 5 | $0.00003 | $0.00042 |
| Sonnet 5 | $0.00001 | $0.00017 |
| Haiku 4.5 | $0.00001 | $0.00008 |
Grade A, and why
eval 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 10d 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.
What it actually says
Eval Command
View agent evaluation history and detect performance regressions.
See skills/eval/SKILL.md for full documentation.
Usage
/team-shinchan:eval
/team-shinchan:eval --agent bo
/team-shinchan:eval --regression
/team-shinchan:eval --compare
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.
- 10d ago First seen · 19 lines · 7 tokens per session scan A 22581292e5d8
eval is a command published in the GitHub repository seokan-jeong/team-shinchan (8 stars, last pushed 1mo ago), licensed MIT. It adds 7 tokens to every session and 84 once invoked, about $0.0000 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.
Other commands, from other repositories
gbu-retro
Post-session retrospective — harvest this session's lessons into durable doctrine.
gbu
Run the tiered multi-agent orchestration loop on a large task.
gbu-audit
Scope-and-rank phase only — a ranked findings list, no delegation.
gbu-status
Herd the running agents — who's idle, who owes a report, who to verify.
typescript-fix
Run TypeScript type-checking and resolve all errors using the typescript-expert skill.
chronicle
One-time codebase onboarding — interactively extracts your team's conventions, identifies reference files, and generates CLAUDE.md sections so Claude codes the way your team does. Run once per project.