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 ccoalm/ccl-skills --skill miniapp-product-devgit clone --depth 1 https://github.com/ccoalm/ccl-skillsWrote 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/ccoalm/ccl-skills/miniapp-product-dev)<a href="https://agentmods.dev/skills/ccoalm/ccl-skills/miniapp-product-dev"><img src="https://agentmods.dev/badge/skills/ccoalm/ccl-skills/miniapp-product-dev/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/ccoalm/ccl-skills/miniapp-product-dev"><img src="https://agentmods.dev/badge/skills/ccoalm/ccl-skills/miniapp-product-dev.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 | $0.00106 | $0.10051 |
| Opus 5 | $0.00053 | $0.05025 |
| Sonnet 5 | $0.00021 | $0.02010 |
| Haiku 4.5 | $0.00011 | $0.01005 |
Grade A, and why
miniapp-product-dev 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 2d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Miniapp Product Dev
Mini-program client engineering and product-facing delivery across WeChat, Alipay, Douyin/TikTok, Baidu and similar hosts; excludes product strategy, backend architecture and visual design. After code/test edits, self-check and invoke code-review automatically before completion.
Framework Scope
Primary framework target: Taro (React-based multi-platform mini-program framework). Apply this skill's recipes assuming Taro unless the task states otherwise.
Adjacent frameworks recognized but not the default target:
- uni-app / Remax / kbone / mpvue: recipes here usually apply with framework-equivalent substitutions; verify before assuming behavior.
- Native WeChat / Alipay / Douyin / Baidu mini-program: host-platform contracts (config, capabilities, review, release) carry over; framework-specific code mechanics (JSX/hooks vs WXML/WXSS, lifecycle hooks, runtime APIs) require native-platform substitution.
- Embedded H5 inside a mini-program webview: this skill owns the mini-program shell, bridge contract, host capabilities, and review/release evidence; the H5 React/Vue page belongs to
web-react-dev(or the relevant web skill).
If the team adopts a non-Taro framework as a new primary, the skill scope must be re-declared rather than letting Taro recipes silently apply to mismatched runtimes.
For evaluating whether Taro is the right choice for a given project (vs native, uni-app, Remax/kbone, or splitting per-platform), read references/framework-choice.md. Taro is the current default but not a permanent decision — re-evaluate when the project's target-platform set, design-system divergence, or maintenance signals change materially.
Maturity Baseline
The current baseline is vendor-spec + framework-canonical, not mature confirmed: host-platform guidelines, Taro documentation/examples, and canonical Taro UI libraries (taroify, NutUI-Taro, tdesign React mapping). No production-quality miniapp portfolio has been observed end-to-end. Apply positive rules as defaults and anti-patterns as guardrails, and mark them confirmed only after a correction-free real feature delivery.
What ships with it
9 files 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.
- agents/openai.yaml 267 B
- references/contracts-and-state.md 11 KB
- references/cross-stack-alignment.md 12 KB
- references/framework-choice.md 9.4 KB
- references/online-practice-uptake.md 7.0 KB
- references/platform-capabilities.md 16 KB
- references/product-page-checklist.md 2.7 KB
- references/qa-release.md 8.8 KB
- references/source-evidence-map.md 11 KB
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.
- 2d ago Changed c8d4797cd301
- 3d ago Changed · +15 tokens per session d47b687c9b38
- 10d ago First seen · 205 lines · 91 tokens per session scan A 9342963c25cf
miniapp-product-dev is a skill published in the GitHub repository ccoalm/ccl-skills (6 stars, last pushed today), licensed Apache-2.0. It adds 106 tokens to every session and 10,051 once invoked, about $0.0005 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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