Awesome-Prompt-Engineering
Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
GraphCanon updated 4w · GitHub synced 4w · 25 views this month
Decision brief
Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
Good fit when
- You need focused materials on GPT and related models for prompt engineering
- Your project uses or plans to use TypeScript
Avoid when
- The project requires languages other than TypeScript
- Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
Observed Jul 16, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 4w
- Provenance
- Not a fork · Organization account
- As of 4w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install Awesome-Prompt-EngineeringHow it fits your stack(1)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides comprehensive materials related to prompt engineering including GPT, ChatGPT, and PaLM models.
Capability facts
- Languages
- typescript
Source: github.language · Jul 28, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 28, 2026)
code, run experiments in-browser, and feed results back into the loop without a Python setup.Source link
Tags
README
Platform Ports & Hardware Forks
- gianfrancopiana/openclaw-autoresearch — OpenClaw port of pi-autoresearch; autonomous experiment loop for any optimization target with statistical confidence scoring.
- miolini/autoresearch-macos — Widely adopted macOS fork that adapts upstream autoresearch for Apple Silicon / MPS while preserving the original loop shape.
- trevin-creator/autoresearch-mlx — MLX-native Apple Silicon port that keeps the upstream fixed-budget
val_bpbloop while removing the PyTorch/CUDA dependency entirely. - jsegov/autoresearch-win-rtx — Windows-native RTX fork focused on consumer NVIDIA GPUs, with explicit VRAM floors and a practical desktop setup path.
- iii-hq/n-autoresearch — Multi-GPU autoresearch infrastructure with structured experiment tracking, adaptive search strategy, crash recovery, and queryable orchestration around the classic
train.pyloop. - lucasgelfond/autoresearch-webgpu — Browser/WebGPU port that lets agents generate training code, run experiments in-browser, and feed results back into the loop without a Python setup.
- tonitangpotato/autoresearch-engram — Fork with persistent cognitive memory — frequency-weighted retrieval of cross-session knowledge for improved experiment continuity.
- Colab/Kaggle T4 port — Adapts autoresearch for free T4 GPUs (Google Colab / Kaggle) with zero cost and zero local setup. Key changes: Flash Attention 3 → PyTorch SDPA, removes H100-only kernel dependency.
- ArmanJR-Lab/autoautoresearch — Jetson AGX Orin port with a director — a Go binary that acts as a "creative director" injecting novelty (arxiv papers + DeepSeek Reasoner) into the loop to escape local minima. Includes multi-experiment comparison (baseline vs director-guided) with detailed stall analysis.
For agents
This page has a .md twin and JSON over the API.