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/manage-skills)<a href="https://agentmods.dev/commands/seokan-jeong/team-shinchan/manage-skills"><img src="https://agentmods.dev/badge/commands/seokan-jeong/team-shinchan/manage-skills/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/manage-skills"><img src="https://agentmods.dev/badge/commands/seokan-jeong/team-shinchan/manage-skills.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 | $0.00010 | $0.00168 |
| Opus 5 | $0.00005 | $0.00084 |
| Sonnet 5 | $0.00002 | $0.00034 |
| Haiku 4.5 | $0.00001 | $0.00017 |
Grade A, and why
manage-skills 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 12d 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
Manage-Skills Command
Analyzes changed files in the team-shinchan plugin and maps them to the appropriate verify-* skills. Detects coverage gaps and reports uncovered areas.
See skills/manage-skills/SKILL.md for full documentation.
Usage
/team-shinchan:manage-skills
When to Use
- After completing a significant feature or refactor
- When
verify-implementationreports many uncovered files - Periodically to maintain skill coverage
What It Does
- Collects recently changed files (via
git statusandgit diff) - Maps each file to its corresponding verify-* skill
- Reports coverage gaps
- Lists recommended validator commands to run
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.
- 12d ago First seen · 29 lines · 10 tokens per session scan A 336f1d3de539
manage-skills is a command published in the GitHub repository seokan-jeong/team-shinchan (8 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 168 once invoked, about $0.0001 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
prompt-evolve
Closed prompt-evolution loop: turn a lesson into a candidate agent prompt, gate it on held-out evals, promote only if it beats the baseline. SIA Meta→Target→Feedback ported to greatcto.
starlight-eval
Run a Starlight Proving Ground pass — evaluate the whole Starlight Intelligence System across seven lanes, render per-lane and system verdicts with the Luminor kernel mindset, and write a scorecard receipt.
bugfix
TDD-driven bugfix workflow: tester writes failing test (RED) → developer fixes (GREEN) → developer refactors (REFACTOR) → reviewer validates. Accepts issue number, description, or both. Auto-creates PR unless --no-pr flag is passed.
develop
Implement skill development issues with TDD-governed workflow.
test
Run the test suite (MPM-provided command — may be shadowed by a user command of the same name).
iterate
Post-implementation iteration loop for bugs found by hand — reproduce the bug, root-cause it, fix it on the current branch with a regression test, run one correctness review pass, and bank the lesson to pipeline memory.