prompts.chat

Quick Verdict

prompts.chat is an active, hosted, open-source prompt ecosystem, formerly known as Awesome ChatGPT Prompts. It combines a browsable public library, downloadable Markdown and CSV data, a self-hostable site, a CLI, a Claude Code plugin, and MCP access. It is useful for discovering starting points, studying prompt structures, or building an internal catalog. It is not an AI model, a guarantee of output quality, or a subscription service with a documented commercial service level.

The licensing split matters. Source code and site-authored content are under MIT; public prompt content and data are dedicated under CC0; the separately distributed MCP package uses ISC. “Open source” does not make those three layers interchangeable. Public submissions may be copied into Git history, datasets, caches, or third-party clients, so deletion cannot guarantee that every copy disappears. Keys, customer records, and confidential prompts should not be submitted publicly. Even with self-hosting, privacy depends on database access, logs, backups, administrators, identity providers, and the model services connected to the installation.

Best For

• Individuals who want examples for role prompts, writing tasks, analysis structures, or reusable output formats.
• Technical teams willing to operate a database, authentication, upgrades, backups, and review controls for a private prompt catalog.
• Educators, researchers, and product teams comparing how prompt wording behaves across different assistants.
• Developers who want CLI or MCP retrieval and can run external content with narrowly scoped permissions.
• It is not ideal for anyone expecting permanent secrecy, centrally reviewed content, guaranteed model results, or a safe place to paste API keys.

Key Features

• Public prompt catalog: Browse community submissions on the site or consume PROMPTS.md, CSV, and dataset forms.
• Multiple interfaces: Use prompts through the website, CLI, remote or local MCP, and a Claude Code plugin.
• Self-hosting: Deploy a branded prompt library with authentication and a PostgreSQL database under your own operational control.
• Model-independent text: Adapt templates for products such as ChatGPT, Claude, and Gemini, subject to each model’s capabilities and system rules.
• Community contribution: Submit prompts that synchronize into the public project, which increases reach but also requires quality and rights review.
• Layered licensing: Track MIT for code and authored site material, CC0 for prompt data, and ISC for the MCP package.

Use Cases

The safest everyday use is to treat a community prompt as a draft rather than an executable instruction. Find a nearby task, then replace its objective, input fields, constraints, evidence rules, and output schema. A market-research template may provide a useful sequence, for example, but its industry assumptions, date range, citations, and acceptance criteria still need rewriting. Testing the revised prompt against more than one representative input is more informative than trusting its popularity.

For teams, self-hosting can turn reviewed prompts into governed templates. Editors can propose changes, owners can approve versions, and users can retrieve the approved copy through the chosen interface. MCP and plugins make retrieval convenient, but they also expand the trust boundary. Public skills, prompt text, and attached scripts may contain prompt injection, hidden requests, or unsafe commands. Review source, pin versions, disable unnecessary network and file access, and test in a low-privilege environment before connecting the tool to real repositories or business data.

Pricing

The public site, prompt data, source repository, and documented open interfaces are available in free or open-source forms. The project accepts sponsorship, but the available evidence does not support inventing a recurring subscription, paid quota, or commercial support tier. Model calls made after copying a prompt may still cost money under the selected model provider’s terms.

Self-hosting has an operational cost even when the software license has no fee. Budget for PostgreSQL, compute, a domain, identity services, email, monitoring, backups, upgrades, and staff time. Licensing also needs a component-level record: CC0 simplifies reuse of the prompt dataset, while MIT and ISC cover their respective software components. None of them automatically clears third-party text inside a submission, trademarks, personal data, or generated output for commercial use.

Pros

• Active ecosystem spanning the hosted catalog, raw datasets, self-hosting, CLI, plugins, and MCP.
• Open formats make prompts easy to inspect, export, analyze, and reorganize.
• Self-hosting gives teams control over branding, identity, database placement, and review workflow.
• Templates are not tied to a single model vendor and can be tested across assistants.
• The project publishes distinct licenses for software, authored site content, prompt data, and the MCP package.
• Public contributions provide broad coverage and many practical starting points.

Cons

• Community prompts have no universal quality, safety, freshness, or accuracy guarantee.
• Public content may persist in repository history, dataset mirrors, caches, and downstream copies after removal.
• A “private” prompt is only as private as the deployment, logs, backups, administrators, and connected services.
• Remote APIs, MCP, plugins, community skills, and scripts increase prompt-injection and supply-chain exposure.
• The project does not provide the underlying model, so cost, retention, capability, and output quality depend on another provider.
• Self-hosting requires ongoing database operations, dependency updates, vulnerability response, and access reviews.

Alternatives

FAQ

Is prompts.chat free?

The public library, source code, prompt data, and documented open interfaces can be used without a platform subscription. Self-hosting infrastructure and third-party model usage can still create costs. Do not assume an undocumented paid plan or guaranteed support arrangement exists.

Can I use public prompts commercially?

The prompt dataset is released under CC0, while code and site-authored material use MIT and the MCP package uses ISC. You must still review third-party material embedded in a prompt, personal data, trademarks, model-provider rules, and generated output. The repository’s licenses cannot grant rights the contributor did not own.

Will deleting a public prompt erase it everywhere?

No such guarantee is possible. Git history, dataset snapshots, caches, search indexes, and downstream copies may retain it. Never submit credentials, regulated records, customer material, or text that must be retractable.

Are private prompts completely private?

No. The answer depends on whether you use the hosted service or self-host, and on authentication, database permissions, logs, backups, administrator access, analytics, and connected model providers. Map the full data path before using sensitive material.

Are MCP integrations and public skills safe to install?

They should be treated as untrusted until reviewed. Prompt content, skill instructions, and scripts can carry prompt injection or destructive commands. Pin versions, inspect source, restrict network and filesystem permissions, and test with non-sensitive data first.

Should an API key be included in a prompt?

No. Put keys in controlled environment variables or a secret manager and let the client or self-hosted service read them. Keeping credentials out of prompts also keeps them out of public submissions, screenshots, model context, and routine logs.

Bottom Line

prompts.chat is valuable because it connects an open prompt collection with downloadable data, self-hosting, and practical tool integrations. Individuals can use it as an idea and structure library. Teams should add review, versioning, permissions, retention rules, and an exit process before treating it as infrastructure. The public catalog is an input source, not a trusted execution boundary.

A sensible evaluation uses five real tasks. Record the original template, every modification, the model used, output quality, failure modes, and any data sent to external services. Keep the prompt only if it repeatedly saves effort and remains understandable to reviewers. Reject workflows that require secrets in prompt text, run unreviewed scripts, or leave the location and retention of business data unclear.