deepseekplugins

DeepSeek Harness plugin

dsh-skill-karpathy-guidelines

Bundled Karpathy coding-guidelines skill provider for DeepSeek Harness

Models & Providers
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Source facts

Repository
oThTJx/dsh-skill-karpathy-guidelines
Latest update
Aug 19, 2026
Category
Models & Providers
GitHub stars
0
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/oThTJx/dsh-skill-karpathy-guidelines
Plugin: dsh-skill-karpathy-guidelines
Author: oThTJx

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer3 files
README.mdSource · read only
README language

@firefly0621/dsh-skill-karpathy-guidelines

English | 中文

Optional bundled skill provider that contributes karpathy-guidelines to ctx.skills. The skill body is adapted from multica-ai/andrej-karpathy-skills (skills/karpathy-guidelines/SKILL.md): think before coding, simplicity first, surgical changes, and goal-driven execution. Upstream package metadata and skill frontmatter declare MIT; see assets/NOTICE for the recorded provenance (the upstream tree currently has no root LICENSE file).

Mount the plugin to enable the provider. It has no configuration. This package is not part of the official main dsh-base composition; install it explicitly with dsh plugin add or a custom cordis.patch.yml.

The loaded definition carries alwaysApply: true. That flag alone does not inject the body: mount @firefly0621/dsh-skill-always-apply (opt-in; not in default dsh-base) so the consumer contributes the packaged instructions to the system prompt of every model request. Without that consumer, the skill remains catalog + on-demand skill tool load only.

The provider exposes its packaged assets/ directory as the skill resource base. Project-local standing orders remain the job of dsh-agent-instructions (AGENTS.md / CLAUDE.md).

Model Experience

Indirectly, through @deepseek-ai/dsh-tool-skill, which renders the catalog entry and selected skill body. With dsh-skill-always-apply mounted, also through that consumer's always-apply system-prompt section.

#### KV Cache effect

When mounted, its catalog entry and any loaded or always-applied body change the provider KV prefix at their insertion points. Setting the composition row to disabled: true removes that effect.

Known Limitations and Deferred Work

  • The provider contributes one fixed skill and has no runtime customization.
  • Upstream CLAUDE.md / Cursor rule packaging is not mirrored; only the skill body is bundled for the dsh skill catalog.
  • Provenance follows upstream-declared MIT metadata rather than a root LICENSE file in that repository; operators who need a stronger redistribution record should re-verify upstream licensing before redistribution.
  • Always-on injection requires a separate always-apply consumer; this package does not inject on its own.
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