DeepSeek Harness is an open-source framework for building and running AI agents, organized so that its functionality is provided through plugins. Developers use it to launch agent workflows through a local web interface or from source, while the catalogue add-ons extend the harness as plugins.
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 deepseek-ai/deepseek-harness --skill office-rendergit clone --depth 1 https://github.com/deepseek-ai/deepseek-harnessWrote 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/deepseek-ai/deepseek-harness/office-render)<a href="https://agentmods.dev/skills/deepseek-ai/deepseek-harness/office-render"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/office-render/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/deepseek-ai/deepseek-harness/office-render"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/office-render.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 | $0.00016 | $0.00976 |
| Opus 5.5 | $0.00006 | $0.00390 |
| Sonnet 5.5 | $0.00003 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
office-render 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 today.
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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document images and visual inspection
Use the installed LibreOffice Kit CLI through the shell with the absolute Node and CLI paths supplied below. In Bash, invoke "<node>" "<cli>"; in PowerShell, invoke & "<node>" "<cli>". Prefer bundled LibreOffice unless the user requests another installation. If the Office sidecar is missing, follow sidecar setup: ask whether the user wants to download it and install only after they agree. Do not invoke system soffice, use npx, install a replacement renderer, or build an alternative rendering pipeline to work around a failed render with an installed sidecar.
Before generating images only for inspection, establish that the current model accepts images. Otherwise finish the applicable content and structure checks and report the requested visual check as unavailable. Do not generate unreadable previews, retry image reads, or choose a second model. Requested image exports can still be delivered.
Render the saved deliverable
Keep the saved deliverable in the task workspace. For requested image exports, use the requested output directory and retain the images. For inspection-only previews, create a private, uniquely named temporary parent inside that workspace and pass a child path that does not yet exist as --output-dir; the CLI creates that child. On POSIX, use preview_root=$(mktemp -d "./.dsh-office.XXXXXXXX") (owner-only permissions) and --output-dir "$preview_root/render". On PowerShell, create a parent named .dsh-office- plus [guid]::NewGuid().ToString('N') under the task workspace and pass an uncreated render child; it inherits the workspace ACL. Quote each input and output path. Retain the parent until image inspection finishes, then remove only that parent, including after a failed render. Never use a fixed directory name or remove the workspace itself. Report inspection results in text; the reply must not link or embed removed previews.
"<node>" "<cli>" render --input <final-file> --output-dir <new-directory> --pages "1,3,4"
"<node>" "<cli>" render --input <final-workbook.xlsx> --output-dir <new-directory> --sheet <exact-name> --range <A1-range>
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.
- today Changed 7ad77f63958b
- yesterday First seen · 32 lines · 16 tokens per session scan A 8313e69261c8
office-render is a skill published in the GitHub repository deepseek-ai/deepseek-harness (250,433 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 976 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-15.
Other skills, from other repositories
manage-taskboard
Manage work in the native DeepSeek Harness Taskboard with exact task ids and optimistic versions. Use when an Agent must inspect project work, claim an eligible todo, record progress or blockers, verify an implementation, submit it for human review, or release its own claim; also use when a human asks how to accept…
industry-research-method
A Chinese-language method for researching an industry by mapping its supply chain, tracking policies and news, and writing sourced reports. It separates the chain into upstream inputs, midstream production, and downstream distribution or use.
company-research-method
A documented process for researching a company using supplied files and selected public sources. It produces a company overview with business details, sourced financial figures, and stated risks.
write-docs-cn
A guide to writing project documentation, especially README files. It explains how tutorials differ from reference material and where each kind of information belongs.
write-tech-cn
A writing guide for Chinese technical documents such as READMEs, tutorials, blogs, and technical explanations. It focuses on direct conclusions, clear actions, natural Chinese, and consistent technical terms.
pptwise
Generate a native, editable PPTX deck from an outline, notes, or source material with the pptwise CLI. Use when the user asks to create a PPT, deck, presentation, or slides and wants a deterministic, editable, theme-consistent result.