Borrowing it
Nothing to install: this file belongs to whitelonng/dshcode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/whitelonng/dshcode/master/.agents/skills/dsh-doc/SKILL.mdgit clone --depth 1 https://github.com/whitelonng/dshcodeWrote 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/whitelonng/dshcode/dsh-doc)<a href="https://agentmods.dev/skills/whitelonng/dshcode/dsh-doc"><img src="https://agentmods.dev/badge/skills/whitelonng/dshcode/dsh-doc.svg" alt="Measured on agentmods" 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 | $0.00095 | $0.03475 |
| Opus 5 | $0.00048 | $0.01737 |
| Sonnet 5 | $0.00019 | $0.00695 |
| Haiku 4.5 | $0.00010 | $0.00347 |
Grade A, and why
dsh-doc 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 yesterday.
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
92% identical to dsh-doc — 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepSeek Harness documentation
Summary
The DeepSeek Harness documentation standard: make every page searchable, newcomer-readable, and exact enough for agents and maintainers, and keep the documentation website a tested projection of repository Markdown. Apply repository AGENTS.md files and executed gates first, then this workflow for kind-mapped metadata, progressive detail, line-aligned bilingual pages, corpus audits, and website publication. Preserve one owner per fact: source, tests, generated catalogs, package READMEs, guides, Agent Notes, and scratch each keep their own kind of truth. The session-persistence-jsonl README pair is the reference example of the format.
Table of Contents
- Workflow
- Fact-check procedure: test, do not assume
- Kind system and templates
- Voice rules
- Quality criteria
- Audit the corpus
- Wordcount budgets
- Website publication
- Detailed references
- Validation
- Dev Note
Workflow
Follow this sequence for each requested scope. Keep the common reader path brief, but do not delete failures, ownership, limitations, or other required contracts merely to reduce words.
- Read root and more-specific
AGENTS.md, the documentation standard, the target page, its source/tests, navigation owner, and bilingual record. - Classify the page by one primary job and reader: product quick start, user task guide, contributor tutorial, architecture overview, package/subsystem reference, generated reference, agent instruction, decision record, or scratch.
- Place the page at its nearest owner. Keep package contracts beside package code; use
docs/for cross-package learning, user, developer, architecture, discussion, and expiring scratch material. - Define the reader's starting state, observable outcome, likely failure, recovery path, and next useful depth before writing details.
- Add or revise YAML metadata — assign the
kindthat maps to the template for this document's job — then writeSummary,Table of Contents, user-facing content, developer-facing content, optionalFurther Exploration, and finalDev Notein that order where the document type permits. - Update the bilingual counterpart in the same pass. Keep headings, lists, tables, code, links, frontmatter layout, and physical line count aligned.
- Verify every claim against code, tests, generators, package metadata, or a current decision owner — and run the operations the page instructs, per the fact-check procedure below. Update the owner before any derivative artifact.
- Run focused checks, then
pnpm run test:docs,pnpm run doc-sync,pnpm run lint, andgit diff --check; re-read the complete diff for correctness and then for brevity and repository fit.
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.
- references/metadata-links-i18n.md 7.5 KB
- references/review.md 5.7 KB
- references/structure-hierarchy.md 6.0 KB
- references/style.md 3.9 KB
- references/website-sync.md 6.7 KB
- templates/package-bundle.md 3.2 KB
- templates/package-group.md 1.7 KB
- templates/package-library.md 3.4 KB
- templates/package-reference.md 3.7 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.
- yesterday First seen · 129 lines · 95 tokens per session scan A 0de6b482d4bc
dsh-doc is a skill published in the GitHub repository whitelonng/dshcode (712 stars, last pushed yesterday), licensed MIT. It adds 95 tokens to every session and 3,475 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to dsh-doc, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
visionary-cli
Analyze images with DeepSeek's vision model via the visionary-server CLI. Use this whenever the user provides an image, photo, screenshot, or document with images - run vision to look at it rather than guessing.
deepseek-harness
Use when building AI agent applications with a plugin-based architecture — Web UI, CLI, Python SDK, Cordis plugin system, multi-model orchestration. DeepSeek Harness (dsh): open-source agent harness by DeepSeek AI where everything is a plugin, powered by Cordis for spatiotemporal composability.
deepseek-harness
Use this skill whenever the user wants to call DeepSeek V4-Pro / V4-Flash (or its legacy aliases deepseek-chat / deepseek-reasoner), or you see code that imports from openai import OpenAI with baseurl="https://api.deepseek.com". This skill teaches you the 10 protocol contract rules required to avoid the 16 documented…
vision-multimodal
给 DeepSeek 这类纯文本模型补上「眼睛和耳朵」的完整视觉/听觉能力 —— 读图、OCR 取字、物体检测定位、视频理解、语音转写、多说话人分离、音乐分析、文档/PDF 可视化、截图直读、图像分割。双通道:默认走 MCP 工具(通义千问),也可用 vision.py 接任意 OpenAI 兼容供应商。.
novel-import-tomato-reference
用于处理番茄小说、Tomato Novel Downloader、免费小说 epub、下载器结果导入、epub 转 Markdown、以及把外部小说素材整理到当前小说 Project Workspace 的 reference/tomato/ 供后续拆书分析。.
web-ui-motion
Build polished front-end UI together with a signature motion effect in a single self-contained HTML file, combining SVG.js / SVG filters and the Canvas 2D API. Use when the user asks to build a web page, landing page, dashboard, or component that has a special visual effect, animation, particle system, fluid/water…