DSXiangLi/DecryptPrompt resource
总结Prompt&LLM论文,开源数据&模型,AIGC应用 observed · 2026-08-28
Health v2 · maintenance only
67/100
- Activity 80
- Release rhythm 35
- Longevity 92
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1300
- days_rel: n/a
- days_push: 120
- n_releases_24m: 0
Adoption not part of the score
3434 stars · 322 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A curated Chinese-language collection of LLM and prompt engineering resources, including open-source models, datasets, frameworks, and AIGC applications, paired with a long-running blog series explaining prompt and LLM papers. It serves as a learning hub rather than usable software.
Use cases
- learn prompt engineering from paper summaries
- find open-source LLM datasets and models
- understand RLHF and instruction tuning
- study chain-of-thought reasoning techniques
- explore LLM agent and RAG research
- keep up with AIGC applications
When to choose
- you want curated paper explanations and resource lists for LLM research
- you prefer Chinese-language learning material on prompt engineering
When to avoid
- you need runnable code or a library to integrate
- you need English-only documentation
Facets
learning-resource · maturity active
prompt-engineering rag llm-training agent-framework large-language-models tutorials artificial-intelligence python awesome-list papers llm chain-of-thought instruction-tuning aigc chinese-language
1 source
- readme: https://github.com/DSXiangLi/DecryptPrompt · fetched 2026-08-28 · 0721e33d7f15
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| DSXiangLi/DecryptPrompt | main | 67 |
For agents
markdown · JSON · MCP: product_card(name="DSXiangLi/DecryptPrompt")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem