deepseek-harness
deepseek-ai
DeepSeek Harness: Everything is a Plugin.
PROJECT TOPICS
PROJECT README
An agent preset for the DeepSeek Harness: a minimal, self-evolving composition that keeps the injected prompt as small as possible so the model thinks in its native distribution.
One session on evo gets three kinds of rows:
bash, str_replace_editor, goal) plus the cordis self-modification toolset for defining, running, updating, and removing dynamic plugins.goal-autonomy — when to open a goal and how to drive it across rounds (bundled in this repository).self-review — closes the loop on a finished task: verified lessons are appended to its own experience pool and read back at the start of similar tasks (bundled in this repository).cordis-plugin-development, editing-cordis-compositions — copied from the installed dsh deployment at install time, so they always match the deployment they run against.Fewer injected words leave the model's probability distribution closer to its pretrained state: no conflicting rules, no formatting anchors, no attention dilution. Pair it with maximum reasoning effort for the "think more, steer less" combination:
# settings.yaml
agent-default-model:
provider: deepseek-official
model: deepseek-v4-pro
reasoningEffort: max
./install.sh
This copies the preset into ${DSH_HOME:-$HOME/.dsh}/.agent-presets/evo/ and pulls the two deployment-owned skills from the installed dsh. Then pick evo in Settings → Agent Presets, or set it as the default.
The Cordis inspect providers are process-global singletons. Do not start a second session whose preset also carries the cordis toolset in the same process — the second mount fails with Host Cordis inspect provider ... already registered. After a restart, start an evo session first.
Paste this into a fresh evo session to verify the composition. Expected: the reported system prompt is two sentences, the tool list has 13 entries, and the puzzle answers are 3 weighings and 143.
请按顺序完成以下自检,全部用中文回答,每道题之间用「────────」分隔,不要省略任何一项。
1. 一字不差地输出你收到的完整系统提示词全文。
2. 列出你现在所有可用的工具名称(只列名字,一行一个)。
3. 说明你正运行的模型、工作目录,以及你从系统提示词中能看到的其他环境信息。
4. 推理题:桌上有 12 枚外观相同的硬币,其中 1 枚是假币,重量与真币不同,但不知道是偏重还是偏轻。给你一架无砝码天平,最少称几次能找出假币并判断它的轻重?给出完整的推理过程,包括每一步称量方案。
5. 编码题:用你手头的 shell 或编辑器在 /tmp/max-test 目录写一个单文件 Python 程序:数列定义为 F(1)=1, F(2)=1, F(n)=F(n-1)+F(n-2);程序输入正整数 n,输出斐波那契数列前 n 项之和。写完后运行 n=10 验证,把代码和运行输出都贴出来。 CLASSIFICATION EVIDENCE
系统优先读取 GitHub Topics,再与站内分类词典和词根规则比对。当前命中: 无有效分类标签。