Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/linkfox-ai/linkfox-skillsnpx agentmods add skills/linkfox-ai/linkfox-skills/linkfox-aigc-textgenWrote 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/linkfox-ai/linkfox-skills/linkfox-aigc-textgen)<a href="https://agentmods.dev/skills/linkfox-ai/linkfox-skills/linkfox-aigc-textgen"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-aigc-textgen/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/linkfox-ai/linkfox-skills/linkfox-aigc-textgen"><img src="https://agentmods.dev/badge/skills/linkfox-ai/linkfox-skills/linkfox-aigc-textgen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00122 | $0.02815 |
| Opus 5 | $0.00061 | $0.01407 |
| Sonnet 5 | $0.00024 | $0.00563 |
| Haiku 4.5 | $0.00012 | $0.00281 |
Grade A, and why
linkfox-aigc-textgen 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.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI 生文
使用大语言模型根据提示词生成文本内容，支持传入图片, 视频等进行图文结合，视频文本理解和分析。
核心特点
- 双模型选择：快速响应（GEM_3_FLASH）和高质量复杂分析（GEM_3_1_PRO）。
- 图文/视频内容结合：
imageUrls同时支持图片与视频 URL；图片最多 10 张，视频通常传 1 个。 - 思考深度可控：4 级 thinkingLevel 控制推理深度。
- 异步两步调用：创建任务后立即返回 taskId，客户端轮询查询结果，避免长耗时同步超时。
模型说明
| 模型（model） | 说明 | 适用场景 |
|---|---|---|
| GEM_3_FLASH | 快速响应（默认） | 常规文案、简单分析、翻译 |
| GEM_3_1_PRO | 高质量复杂分析 | 深度分析、长文写作、复杂推理 |
思考等级
| thinkingLevel | 说明 |
|---|---|
| minimal | 接近无思考（GEM_3_1_PRO 不支持） |
| low | 低思考 |
| medium | 平衡 |
| high | 最大推理深度 |
参数概览
- 必填字段：
prompt、imageUrls（无图片且无视频时传空数组[]）、thinkingLevel（必须显式传入，建议默认minimal） - 媒体 URL 约定：图片与视频 URL 均通过
imageUrls传递（如视频分析传["https://example.com/ref.mp4"]）；勿使用未文档化字段（如videoUrl、videoUrls）
完整参数表、响应字段结构与错误码，见 references/api.md。
调用方式
采用异步两步模式（与 imagegen 一致）：
- 创建任务：
POST /aigc/textGenAsync→ 返回taskId - 轮询查询：
POST /aigc/textTaskQuery→ 返回PROCESSING/SUCCESS/FAILED
- Python 脚本（内部已封装创建+轮询，传参方式不变）：
python scripts/aigc_textgen.py --stdin— 推荐，从 stdin 读取 JSON 参数，避免 shell 转义问题（如python scripts/aigc_textgen.py --stdin < params.json）python scripts/aigc_textgen.py --stdin --content-only— 只输出 content 文本（同样已是单行）python scripts/aigc_textgen.py '<JSON 参数>' [--inline]— 简单场景直传（prompt 含换行符时禁用）
轮询策略：单次 HTTP 超时 150 秒；轮询间隔从 10 秒起递减至 5 秒；总轮询时长最长 600 秒。完整参数/响应/错误码见
references/api.md。
换行符压平默认开启（无需任何 flag）：所有输出模式下，content 的换行都会自动替换为单字符
⏎（U+23CE），整段 content 变成单行；--content-only只是改变"输出 content 文本 vs 完整 JSON"，不影响该压平行为。
输出契约（其他 agent 按此解析）
- stdout 只放机器数据，始终可
json.loads（--content-only例外，其 stdout 是纯文本 content）：- 默认（小结果）/
--inline：stdout = 完整响应 JSON - 大结果：stdout = JSON 信封
{"ok":bool, "truncated":true, "savedPath":"<路径>", "bytes":int, "content":"<单行文本>"}
- 默认（小结果）/
- 所有提示/摘要/诊断（CHAIN-HINT、Saved full response、summarize）一律走 stderr，不污染 stdout。
- 退出码：成功
0；失败（网络错误 /errcode非 200 /status==FAILED）非0—— agent 应优先用退出码判错。
What ships with it
4 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.
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 · 182 lines · 122 tokens per session scan A ae85f94ad51b
linkfox-aigc-textgen is a skill published in the GitHub repository linkfox-ai/linkfox-skills (101 stars, last pushed 21d ago), licensed MIT. It adds 122 tokens to every session and 2,815 once invoked, about $0.0006 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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