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 hhjk21/Firefly-Companion-AI- --skill examplegit clone --depth 1 https://github.com/hhjk21/Firefly-Companion-AI-Wrote 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/hhjk21/firefly-companion-ai-/example)<a href="https://agentmods.dev/skills/hhjk21/firefly-companion-ai-/example"><img src="https://agentmods.dev/badge/skills/hhjk21/firefly-companion-ai-/example/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/hhjk21/firefly-companion-ai-/example"><img src="https://agentmods.dev/badge/skills/hhjk21/firefly-companion-ai-/example.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 | $0.00050 | $0.00238 |
| Opus 5.5 | $0.00020 | $0.00095 |
| Sonnet 5.5 | $0.00010 | $0.00048 |
| Haiku 4.5 | $0.00005 | $0.00024 |
Grade A, and why
example-weather scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. 使用 `run_shell` 工具执行 `curl "wttr.in/{city}?format=4"` 获取天气 What it actually says
天气查询
使用场景
- 用户问"今天天气怎么样"
- 用户问"明天会下雨吗"
- 用户询问"北京气温多少"
执行步骤
- 从用户消息中提取城市名称(中文或英文)
- 如果城市名不明确,友好地询问用户具体指哪个城市
- 使用
run_shell工具执行curl "wttr.in/{city}?format=4"获取天气 - 将返回的天气信息用友好、自然的口吻告诉用户
注意事项
- wttr.in 可能返回英文结果,需要翻译后告知用户
- 如果 curl 失败,提醒用户检查网络连接
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 · 28 lines · 50 tokens per session scan A c8e4825f5f02
example-weather is a skill published in the GitHub repository hhjk21/Firefly-Companion-AI- (31 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 238 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
example-weather compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| example-weatherhhjk21/Firefly-Companion-AI- | Skill | 31 | 3mo ago | MIT | A | 50 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |