OPC-Starter is a React and TypeScript application starter for building AI-friendly products, with authentication, organization management, an agent-driven UI, data synchronization, and reusable interface components. Developers use it as a foundation for a single-person or small-team company application, with mock or real Supabase backends. Its catalogue entries provide the skills, commands, rules, agents, and instructions used to develop the starter with AI coding tools.
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.
npx skills add alibaba/opc-starter --skill bmad-review-edge-case-huntergit clone --depth 1 https://github.com/alibaba/opc-starterWrote 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/alibaba/opc-starter/bmad-review-edge-case-hunter)<a href="https://agentmods.dev/skills/alibaba/opc-starter/bmad-review-edge-case-hunter"><img src="https://agentmods.dev/badge/skills/alibaba/opc-starter/bmad-review-edge-case-hunter/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/skills/alibaba/opc-starter/bmad-review-edge-case-hunter"><img src="https://agentmods.dev/badge/skills/alibaba/opc-starter/bmad-review-edge-case-hunter.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 | $0.00055 | $0.01029 |
| Opus 5 | $0.00028 | $0.00515 |
| Sonnet 5 | $0.00011 | $0.00206 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
bmad-review-edge-case-hunter 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 11d 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 74 lines · 55 tokens per session scan A 03087a3f1e93
bmad-review-edge-case-hunter is a skill published in the GitHub repository alibaba/opc-starter (87 stars, last pushed 1mo ago), licensed GPL-3.0. It adds 55 tokens to every session and 1,029 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 2026-08-30.
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Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or…