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.
Borrowing it
Nothing to install: this file belongs to deepseek-ai/deepseek-harness. 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/deepseek-ai/deepseek-harness/master/.agents/skills/dsh-find-simplifications/SKILL.mdgit 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/dsh-find-simplifications)<a href="https://agentmods.dev/skills/deepseek-ai/deepseek-harness/dsh-find-simplifications"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/dsh-find-simplifications/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/dsh-find-simplifications"><img src="https://agentmods.dev/badge/skills/deepseek-ai/deepseek-harness/dsh-find-simplifications.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00097 | $0.03185 |
| Opus 5 | $0.00048 | $0.01592 |
| Sonnet 5 | $0.00019 | $0.00637 |
| Haiku 4.5 | $0.00010 | $0.00318 |
Grade A, and why
dsh-find-simplifications 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 2d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
- dsh-find-simplifications — 98% identical, 2 lines differ
- freddie-find-simplifications — 95% identical, 33 lines differ
- dsh-find-simplifications — 91% identical, 2 lines differ
- dsh-find-simplifications — 91% identical, 21 lines differ
- dsh-find-simplifications — 91% identical, 21 lines differ
- dsh-find-simplifications — 91% identical, 21 lines differ
- alego-find-simplifications — 89% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finding DeepSeek Harness Simplifications
This skill helps turn a broad "find things to simplify" request into evidence-backed Agent Notes that remove or collapse existing harness surface area. It is guidance, not a checklist: follow the code, keep judgment active, and prefer a few well-proven candidates over a pile of thin guesses.
Start With Repo Context
- Read
AGENTS.md, especially the pre-release stance and the conventions (including the tests-are-not-golden-truth and Agent Notes-are-not-golden-truth doctrines), plus docs/defensive-patterns.md and docs/testing.md. - Skim docs/architecture.md before judging anything under
packages/; simplifications that fight the service map or event taxonomy need extra evidence. - Use the Agent Note tree and its rules to understand intentional architecture. The most relevant implemented examples are drop mutable session summary, handle-based session persistence, JSONL-only first-party Session persistence, capability seams, and the twin-adapter Agent Notes.
- Treat dual LLM adapters as intentional by default. Session persistence is different: JSONL is the sole first-party provider, while the backend-neutral service remains available to out-of-tree providers. Do not propose deleting an LLM twin or the persistence seam as "low effort" unless the user explicitly overrides that constraint. Removing an unused method or hook inside a protected seam can still be valid if it does not collapse the protected design.
What Counts As A Strong Candidate
A strong simplification removes, folds, or demotes something real and has clear evidence that the current design costs more than it buys:
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.
- 2d ago Changed 03d56b6412ab
- 7d ago Changed d29fc5fe6958
- 8d ago Changed · +4 lines edcbe3e32198
- 11d ago First seen · 154 lines · 97 tokens per session scan A 3f062460e562
dsh-find-simplifications is a skill published in the GitHub repository deepseek-ai/deepseek-harness (218,563 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 3,185 once invoked, about $0.0005 per session on Opus 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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