--- title: "prompt-master vs Agent-Reach" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nidhinjs-prompt-master-vs-panniantong-agent-reach" tools: ["nidhinjs-prompt-master", "panniantong-agent-reach"] --- # prompt-master vs Agent-Reach *GraphCanon updated Jul 11, 2026* ## Verdict Pick prompt-master when tags unique to prompt-master: claude-ai, claude-skills, llm, prompt-engineering; pick Agent-Reach when tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. [prompt-master](https://github.com/nidhinjs/prompt-master) reports 10k GitHub stars, 1.2k forks, and 19 open issues, last pushed Jun 10, 2026. [Agent-Reach](https://github.com/Panniantong/Agent-Reach) has 55k stars, 4.5k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [prompt-master's repository](https://github.com/nidhinjs/prompt-master) and [Agent-Reach's repository](https://github.com/Panniantong/Agent-Reach). | | [prompt-master](/tools/nidhinjs-prompt-master.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Tagline | A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention | Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees. | | Stars | 10,398 | 54,715 | | Forks | 1,236 | 4,509 | | Open issues | 19 | 144 | | Language | - | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks | LLM Frameworks, AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [prompt-master](/tools/nidhinjs-prompt-master.md) | [Agent-Reach](/tools/panniantong-agent-reach.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 30d | 0d | | Open issues (now) | 19 | 144 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/nidhinjs-prompt-master/trust.md) | [trust report](/tools/panniantong-agent-reach/trust.md) | ## Choose when ### Choose prompt-master if… - Tags unique to prompt-master: claude-ai, claude-skills, llm, prompt-engineering. - Leaner open-issue backlog (19). ### Choose Agent-Reach if… - Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code. - Also covers AI Agents, Developer Tools. - More GitHub stars (55k vs 10k) - visibility, not fit. ## 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. ## When NOT to use Agent-Reach - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## Common questions ### What is the difference between prompt-master and Agent-Reach? prompt-master: A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention. Agent-Reach: Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.. See the comparison table for live GitHub stats and shared categories. ### When should I choose prompt-master over Agent-Reach? Choose prompt-master over Agent-Reach when Tags unique to prompt-master: claude-ai, claude-skills, llm, prompt-engineering; Leaner open-issue backlog (19). ### When should I choose Agent-Reach over prompt-master? Choose Agent-Reach over prompt-master when Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers AI Agents, Developer Tools; More GitHub stars (55k 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 Agent-Reach? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### Is prompt-master or Agent-Reach more popular on GitHub? Agent-Reach has more GitHub stars (54,715 vs 10,398). Stars measure visibility, not whether either tool fits your constraints. ### Are prompt-master and Agent-Reach open source? Yes - both are open-source projects on GitHub (prompt-master: MIT, Agent-Reach: MIT). ### Where can I find alternatives to prompt-master or Agent-Reach? GraphCanon lists graph-backed alternatives at [prompt-master alternatives](/tools/nidhinjs-prompt-master/alternatives) and [Agent-Reach alternatives](/tools/panniantong-agent-reach/alternatives) ([prompt-master markdown twin](/tools/nidhinjs-prompt-master/alternatives.md), [Agent-Reach markdown twin](/tools/panniantong-agent-reach/alternatives.md)), 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](/compare/nidhinjs-prompt-master-vs-panniantong-agent-reach.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, prompt-master or Agent-Reach? prompt-master: Steady. Agent-Reach: Very 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 Agent-Reach? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [prompt-master trust report](/tools/nidhinjs-prompt-master/trust); [Agent-Reach trust report](/tools/panniantong-agent-reach/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nidhinjs-prompt-master`](/api/graphcanon/graph?tool=nidhinjs-prompt-master) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_