Comparison
prompt-master vs awesome
Verdict
Pick prompt-master when license: prompt-master is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, prompt-master is MIT.
Markdown twin · prompt-master alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | prompt-master | awesome |
|---|---|---|
| Maintenance | Steady (30d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- prompt-master
- A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- prompt-master
- 10k
- awesome
- 484k
Forks
- prompt-master
- 1.2k
- awesome
- 36k
Open issues
- prompt-master
- 19
- awesome
- 92
Language
- prompt-master
- -
- awesome
- -
Adopt for
- prompt-master
- -
- awesome
- -
Persona
- prompt-master
- -
- awesome
- -
Runtime
- prompt-master
- -
- awesome
- -
License
- prompt-master
- MIT
- awesome
- CC0-1.0
Last pushed
- prompt-master
- Jun 10, 2026
- awesome
- Jun 30, 2026
Categories
- prompt-master
- LLM Frameworks
- awesome
- LLM Frameworks
Trust and health
Maintenance
- prompt-master
- Steady (60%)
- awesome
- Active (82%)
Days since push
- prompt-master
- 30d
- awesome
- 11d
Open issues (now)
- prompt-master
- 19
- awesome
- 92
Full report
- prompt-master
- Trust report
- awesome
- Trust report
Choose prompt-master if…
- License: prompt-master is MIT, awesome is CC0-1.0.
- Tags unique to prompt-master: claude-ai, claude-skills, llm, prompt-engineering.
- Leaner open-issue backlog (19).
When NOT to use prompt-master
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose awesome if…
- License: awesome is CC0-1.0, prompt-master is MIT.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 10k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (nidhinjs/prompt-master) · observed Jul 11, 2026
- GitHub forks (nidhinjs/prompt-master) · observed Jul 11, 2026
- Last push (nidhinjs/prompt-master) · observed Jun 10, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: prompt-master 10k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between prompt-master and awesome?
- prompt-master: A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose prompt-master over awesome?
- Choose prompt-master over awesome when License: prompt-master is MIT, awesome is CC0-1.0; Tags unique to prompt-master: claude-ai, claude-skills, llm, prompt-engineering; Leaner open-issue backlog (19).
- When should I choose awesome over prompt-master?
- Choose awesome over prompt-master when License: awesome is CC0-1.0, prompt-master is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 10k) - visibility, not fit.
- When should I avoid prompt-master?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is prompt-master or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 10,398). Stars measure visibility, not whether either tool fits your constraints.
- Are prompt-master and awesome open source?
- Yes - both are open-source projects on GitHub (prompt-master: MIT, awesome: CC0-1.0).
- Where can I find alternatives to prompt-master or awesome?
- GraphCanon lists graph-backed alternatives at prompt-master alternatives and awesome alternatives (prompt-master markdown twin, awesome markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, prompt-master or awesome?
- prompt-master: Steady. awesome: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for prompt-master and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: prompt-master trust report; awesome trust report.