dsh-TUI is a terminal interface plugin for DeepSeek Harness that gives the coding agent a Claude Code-style display with live status, streaming thoughts, rollback, context usage, and TPS information. It is for users who prefer a richer terminal workflow, and the catalogue entries extend the DeepSeek Harness environment with related skills and instructions.
Borrowing it
Nothing to install: this file belongs to ccch1mneyyy/dsh-TUI. 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/ccch1mneyyy/dsh-TUI/main/.agents/skills/practice/SKILL.mdgit clone --depth 1 https://github.com/ccch1mneyyy/dsh-TUIWrote 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/ccch1mneyyy/dsh-tui/practice)<a href="https://agentmods.dev/skills/ccch1mneyyy/dsh-tui/practice"><img src="https://agentmods.dev/badge/skills/ccch1mneyyy/dsh-tui/practice/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/ccch1mneyyy/dsh-tui/practice"><img src="https://agentmods.dev/badge/skills/ccch1mneyyy/dsh-tui/practice.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 | $0.00036 | $0.00280 |
| Opus 5.5 | $0.00014 | $0.00112 |
| Sonnet 5.5 | $0.00007 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
practice 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.
What it actually says
Help the user learn one concept through a small exercise and feedback. Use their stated goal, level, and time budget; ask only when the missing context changes the exercise.
- Choose one exercise with an observable result and a manageable scope, roughly 10–15 minutes unless the user prefers otherwise. Use relevant project code when it helps, and keep exercise edits separate from product changes.
- Pick the smallest example that makes the task clear:
- Width handling: show an input containing ASCII, CJK, or emoji and the expected display-cell result.
- Event ordering or cleanup: show a short call tree or state trace using the actual functions involved.
- Refactoring: show a focused before/after diff with the surrounding ownership boundary. Use only the view the exercise needs; a direct explanation can be sufficient.
- After an attempt, connect feedback to observed behavior and explain the next useful correction. Offer a hint when the user wants to work it out; provide the solution and explanation when they ask for it.
- Check the exercise's result before offering a harder variation. Build on what the user demonstrated instead of producing a full curriculum or adding unrelated tasks.
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 Changed · -7 lines · +8 tokens per session 43216933b275
- 1mo ago First seen · 23 lines · 28 tokens per session scan A 8d6d81a23310
practice is a skill published in the GitHub repository ccch1mneyyy/dsh-TUI (5,919 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 280 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 2026-08-30.
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