Awesome-Prompt-Engineering
Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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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 Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
npm install Awesome-Prompt-EngineeringHow it fits your stack(1)
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Similar tools
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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 · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 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 autore...
For agents
This page has a .md twin and JSON over the API.