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 WZZNNE/DSH-CyberWorkStation --skill dsh-local-modelsgit clone --depth 1 https://github.com/WZZNNE/DSH-CyberWorkStationWrote 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/wzznne/dsh-cyberworkstation/dsh-local-models)<a href="https://agentmods.dev/skills/wzznne/dsh-cyberworkstation/dsh-local-models"><img src="https://agentmods.dev/badge/skills/wzznne/dsh-cyberworkstation/dsh-local-models/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/wzznne/dsh-cyberworkstation/dsh-local-models"><img src="https://agentmods.dev/badge/skills/wzznne/dsh-cyberworkstation/dsh-local-models.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 | $0.00138 | $0.01571 |
| Opus 5.5 | $0.00055 | $0.00628 |
| Sonnet 5.5 | $0.00028 | $0.00314 |
| Haiku 4.5 | $0.00014 | $0.00157 |
Grade A, and why
dsh-local-models 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 1mo 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 1mo ago First seen · 45 lines · 138 tokens per session scan A 20bcd39e68d8
dsh-local-models is a skill published in the GitHub repository WZZNNE/DSH-CyberWorkStation (24 stars, last pushed 1mo ago), with no licence file. It adds 138 tokens to every session and 1,571 once invoked, about $0.0006 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 2026-09-12.
Other skills, from other repositories
dsh-troubleshoot
A troubleshooting guide for DeepSeek Harness, including its process, configuration, plugins, tools, skills, and model connections.
error-handling
A set of rules for deciding how software should report failures and what information error messages should contain.
ask-good-questions
Guidance for asking technical questions and reporting bugs so another developer can reproduce and answer them. It covers versions, environment details, minimal reproduction steps, expected and actual results, errors, and attempted fixes.
debug-systematically
A structured method for finding the underlying cause of a software problem. It moves from reproducing the problem, to locating the responsible code, to testing a specific explanation, and then to fixing it with a regression test.
perf-optimize
A performance-improvement guide that requires measuring real behaviour before changing code. It explains how to identify bottlenecks, test one change at a time, and check that the change did not break anything.
skill
Ten native DSH tools give a text-only agent eyes. Use these structured tools directly; do not shell out to the bundled Python scripts or reproduce their implementation. Vision API credentials and model settings are managed by the plugin, so tool calls do not receive credentials.