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 DDDFXYqiming/dsh-vision-skill --skill dsh-vision-skillgit clone --depth 1 https://github.com/DDDFXYqiming/dsh-vision-skillWrote 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/dddfxyqiming/dsh-vision-skill/dsh-vision-skill)<a href="https://agentmods.dev/skills/dddfxyqiming/dsh-vision-skill/dsh-vision-skill"><img src="https://agentmods.dev/badge/skills/dddfxyqiming/dsh-vision-skill/dsh-vision-skill/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/dddfxyqiming/dsh-vision-skill/dsh-vision-skill"><img src="https://agentmods.dev/badge/skills/dddfxyqiming/dsh-vision-skill/dsh-vision-skill.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 | $0.02864 |
| Opus 5 | $0.00025 | $0.01432 |
| Sonnet 5 | $0.00010 | $0.00573 |
| Haiku 4.5 | $0.00005 | $0.00286 |
Grade A, and why
vision 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 9d 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.
This is a copy
100% identical to vision — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
识图技能（DSH 标准插件版）
当主模型不支持直接读取图片时，图片不会进入对话上下文，但本插件现在会：
- 直接贴图：client 插件在粘贴进入 DSH 附件管线前截获图片，输入框显示
📎 image1.png / image2.png ...编号 chip（从 1 开始）；发送时自动上传到工作区.dsh-vision/pasted/并把路径文本交给模型（无需任何框架补丁）； - 或用户消息中本来就带图片本地路径（形如
C:/Users/.../xxx.png）。
图片如何进入工具（三种方式）
本插件的所有工具只接收图片的本地路径（文本），不接收图片本体。路径来源有三种（前两种无需 pi-ai 补丁）：
- 路径直发（最常见）：用户消息中的图片会以路径文本出现——可能是附件占位符（
[图片附件 sha256:...，本地路径 C:\Users\...png，模型不支持直接读图，请用 vision skill 读取]），也可能是用户直接给出路径。直接调用vision_analyze等工具即可。 - 剪贴板：用户说"看图"且图片在剪贴板（如 Win+Shift+S 截屏自动复制）→ 调用
vision_clipboard，它会自动把剪贴板图片保存到工作区.dsh-vision/再识别。 - 直接贴图：本插件自带 paste-to-path client——在输入框粘贴图片时，图片先上传到工作区
.dsh-vision/pasted/，再以路径文本进入消息；消息里没有 image 块，因此 DSH 不会报MODEL_DOES_NOT_SUPPORT_IMAGES。旧 pi-ai 补丁仅作为兼容保留，不再必需。
DeepSeek Harness（DSH）插件模式
本技能已打包为标准 DSH 插件 dsh-vision-skill（工具 + 运行时 skill，无需任何框架补丁）：
vision_analyze：识别指定路径的本地图片（image_path必填；可选mode/prompt/crop/budget）。mode=evidence返回结构化证据 JSON（summary / ocr_full_text / layout 阅读顺序 / semantics 实体关系 / uncertainty）；多 provider 自动 failover，429 自动退避vision_ocr：独立 OCR 工具——提取图片中全部可见文字，保持原始排版（image_path必填；可选prompt/crop/budget）vision_ground：定位工具——在图片中查找指定目标（如「所有按钮」「微信图标」），返回每个目标的像素坐标框（bbox_pixel）与归一化坐标（bbox_normalized，0-1000），可选output保存带标注框的预览图vision_detect：枚举工具——清点图片中某一类元素（默认所有 UI 元素），逐个编号 + 像素坐标框；与vision_ground互补（ground 找一个，detect 数一类）vision_dominant_colors：主色分析——提取图片（或区域）主要颜色与占比（本地像素算法，无需视觉 API），用于取主题色/配色分析vision_long_screenshot_ocr：超长截图分块 OCR——聊天记录/整个网页等超高图自动切块（带重叠）→ 逐块识别 → 合并全文，带块边界信息；每块先跑本地 tesseract（chi_sim+eng），失败自动回退 VLMvision_clipboard：读取剪贴板中的图片，保存到会话工作区.dsh-vision/后识别——用户在输入框粘贴图片被"当前模型不支持图片"拦截时，只需把图片复制到剪贴板（如 Win+Shift+S 截屏自动复制）后说"看图"即可- 渐进式工具暴露：加载本 skill 后自动为当前 Agent 激活上述 7 个识图工具；若工具未出现，调用一次
vision_activate兜底 - 模型配置走插件 config：
apiUrl/model/apiKey（任意 OpenAI 兼容的多模态模型均可接入——如 Qwen-VL、MiniMax-M3、Gemini、GPT-4o 等；默认 MiniMax-M3）。新增visionProviders数组可配置 fallback 链路（顺序=优先级，自动 failover；429 按 Retry-After 退避重试一次）。密钥支持 DSH Credential 引用（credential: VISION_API_KEY），推荐后者避免明文 - 分辨率预算：
budget支持small(≈512²) /normal(≈1024²) /large(≈1448²) /mega(≈4096²，约 16M 像素超高清，对应 Qwen 官方高分辨率模式) - 识别流程：脚本输出描述后原样转述，重要文字、报错信息逐字复述，不概括、不脑补
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.
- 9d ago First seen · 120 lines · 50 tokens per session scan A 6ab7edded864
vision is a skill published in the GitHub repository DDDFXYqiming/dsh-vision-skill (2 stars, last pushed 7d ago), licensed MIT. It adds 50 tokens to every session and 2,864 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to vision, differing in 0 lines, and is treated as a copy.
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