dsh-anchored-standard: Two-phase DeepSeek Harness preset: Minimal-aligned bootstrap, then full Standard tools (Project2 98/99)

by xiaobright · Agent Tool · ★ 3.7k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 70/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

deepseekdeepseek-harnessdsh-pluginllm-agent

Quick Facts

Stars3,742
Forks120
LanguageJavaScript
CategoryAgent Tool
Quality Score70/100
Open Issues20
Last Updated2026-10-21
Created2026-08-14
Platformsnode
Est. Tokens~15k

About dsh-anchored-standard

dsh-anchored-standard 中文说明 An experimental DeepSeek Harness agent preset that bootstraps the first model request with a Minimal-aligned prompt, the Minimal preset's real tool schema ( + ), and no auto-injected workspace/skill context, then exposes the complete Standard tool catalog after the first durable tool call or reply. This is a community project.

Compatible Skills

These tools work well together with dsh-anchored-standard for enhanced workflows:

  • awesome-dsh-plugins — semantic(0.67)+complementary+rare_topics+same_lang+shared_platform (73%)
  • awesome-deepseek-harness — semantic(0.63)+complementary+rare_topics+similar_pop+shared_platform (71%)
  • sandbase-harness — semantic(0.54)+complementary+rare_topics+similar_pop+shared_platform (68%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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