AI AI Apps

Build hosted mini-apps by chatting — describe an app and the assistant deploys it to {slug}.skillsafe.ai.

Turn a one-line objective plus your project facts into a dependency-ordered construction plan any coding agent can execute cold: self-contained context briefs, one-PR-sized steps, real verification commands, observable exit criteria, and parallel waves computed from the dependency graph. Paste an existing plan and the analyzer audits it free in your browser; forks only you can settle come back as open decisions, never guesses. Derived from @affaan-m/blueprint.

Chat with several AI models at the same time and compare their answers side by side. Every reply streams in with a receipt for what it actually cost - worst-case hold up front, charged at actual usage, the rest refunded. Pick up to four models per chat.

Paste a rough AI prompt and get an optimized, self-contained rewrite built on proven prompting frameworks (RTF, RISEN, Chain of Thought, RODES and seven more), plus the gaps found, clarifying questions when details are missing, and a quality self-check. Free instant prompt lint runs in your browser. Derived from @sickn33/prompt-engineering.

Describe the capability you want an AI agent to have - or paste a SKILL.md you already use - and get a complete, well-formed agent skill: trigger-complete frontmatter, an imperative body under the 500-line budget, a bundled-resource plan, and an honest review that raises open questions instead of inventing answers. The SKILL.md linter runs free in your browser. Derived from @tldraw/skill-creator.

Paste your MCP server — TypeScript SDK, Python FastMCP or tool-definition JSON — and get a senior MCP engineer review: a readiness posture, the tool inventory, prioritized findings with corrected fragments, quick wins and focus areas. A free instant prescan flags missing descriptions and schemas, any-typed params, generic errors, unpaginated list tools, missing annotations, literal secrets, stdout logging on stdio and deprecated SSE before you run anything. Derived from @anthropics/mcp-builder.

Paste a task spec and the deliverable produced against it. Two independent AI reviewers check it against your rubric with no shared context - both must pass before it ships. A NICE verdict means it ships; NAUGHTY returns a deduplicated fix plan ordered blockers-first and tagged by which reviewer caught what, plus a reviewer-agreement metric, a paste-ready fix prompt, and a re-check that diffs the revision criterion by criterion. Derived from @affaan-m/santa-method.

Turn a sample API response into the response_semantics block and Adaptive Card templates that make Microsoft 365 Copilot render your plugin data as cards. The free half is a real engine: paste the JSON and it measures every field with type, sample and presence count, generates a grounded skeleton bundle, validates any bundle against the card rules AND against the sample — a binding that resolves on no item is named — and previews the exact card each item would produce. The AI pass shapes the template around your intent, and the same validator re-checks every binding it used, in public. A derived work of @github/mcp-create-adaptive-cards (github/awesome-copilot).

Ask an AI image model to mark up a photo and it returns a subtly re-rendered image rather than your original. This isolates just the red annotations as a transparent PNG - with a sensitivity slider - and can composite them back onto your untouched original, so you get clean marks over pristine pixels. Runs fully client-side; neither image is uploaded. Imported Claude artifact. Source: https://github.com/simonw/tools (red-extractor.html, Apache-2.0).

Paste a vision model's segmentation output - objects with box_2d bounds and base64 masks - and see the boxes and masks drawn over the image, with a switchable co-ordinate origin for when a mask lands mirrored or rotated. Ships an embedded example so it demos without an upload. Runs fully client-side. Imported Claude artifact. Source: https://github.com/simonw/tools (mask-visualizer.html, Apache-2.0).