Megidoc turns any public GitHub repository into polished, ready‑to‑publish artefacts (README, technical docs, product ideas, use‑case reports, road‑maps, marketing copy, even custom Claude AI skills) in seconds, letting developers, writers, product people and open‑source maintainers get the documentation they need without ever leaving their browser.
| Who suffers? | Pain point | Why it matters |
|---|---|---|
| Full‑stack engineers | Writing or updating READMEs and design docs is a low‑priority, time‑eating chore. | Docs fall out‑of‑date → onboarding slows, PRs get rejected. |
| Technical writers / documentation teams | Must read many files, understand architecture, and keep tone consistent across projects. | Manual research is costly and error‑prone. |
| Product managers / founders | Need a quick “value‑prop” or feature‑road‑map from a codebase they’re evaluating. | Decision‑making stalls while they hunt for hidden logic. |
| Open‑source maintainers | Docs drift after refactors; community asks for concrete use‑case examples. | Bad docs = fewer contributors, lower adoption. |
| New contributors / interns | Overwhelmed by large repos with little guidance. | Ramp‑up time spikes, PR quality drops. |
| QA / DevOps engineers | Lack of clear architectural overviews makes test‑plan and CI/CD design hard. | Missed bugs, flaky pipelines. |
Bottom line: Every software team spends hours manually extracting, summarising, and formatting information that an LLM could produce instantly.
| What Megidoc does | Why it’s valuable |
|---|---|
| Pulls a curated slice of a repo (via the GitHub API, limited to the most relevant files) | Guarantees the LLM only sees the code that matters, keeping requests fast and cost‑effective. |
| Streams the request to a generative‑AI model (OpenRouter or Ollama Cloud) and streams the answer back to the UI | Users see the output appear in real‑time, feeling the generation happen live. |
| Offers a menu of output types – README, Technical Docs, Idea Explanation, Use‑Case Report, Feature Suggestions, Marketing Strategy, custom Claude AI Skill, Agent Hooks – all generated from the same repo snapshot | One UI, many deliverables. No need to switch tools or copy‑paste between services. |
| Live preview / raw markdown toggle + copy‑to‑clipboard & download | Immediate visual feedback, zero‑friction hand‑off to the repo or a docs site. |
| Built with a modern, production‑grade UI (Next.js 16, Tailwind, Radix UI, dark‑mode, polished animations) | The experience feels like a commercial SaaS product, not a hacky script. |
| Extensible “skill” and “hook” generators that output reusable Claude AI skill definitions or deterministic shell‑script hooks | Enables teams to embed the same AI‑driven guardrails into CI pipelines or internal agents. |
Result: A single click gives you a high‑quality markdown artefact that would otherwise take hours of manual work.
| Persona | Typical use case |
|---|---|
| Full‑stack Engineer | Generate a fresh README after a refactor. |
| Technical Writer | Produce a complete technical spec for a new repo. |
| Product Manager / Founder | Get a concise “Idea Explanation” and feature roadmap for a repo you’re evaluating. |
| Open‑source Maintainer | Re‑generate docs after a major release, or create a use‑case report for newcomers. |
| New Contributor / Intern | Quickly understand the purpose and common workflows of a project. |
| QA / DevOps Engineer | Pull out architecture diagrams and CI/CD hints for test planning. |
All of these roles can reach the product through a web browser; no local installation or special credentials (other than a public GitHub URL) are required.
ReadableStream that the browser consumes chunk‑by‑chunk. Users see the text appear instantly, which feels faster than waiting for a whole document to be generated.lib/github.ts (not shown but referenced) fetches a fixed‑size, prioritized file set (max 60 files, size caps) so the prompt stays within model limits while still capturing the essence of the codebase.