What is ContextForge?

ContextForge is a generator that turns a snapshot of your repository into the instruction files coding agents read — AGENTS.md, .cursorrules, CLAUDE.md, .github/copilot-instructions.md — plus a machine-readable spec of the detected stack.

You paste a file tree and optionally a few key files. A deterministic extractor infers package manager, framework, language, test runner, CI system, entry points and directory conventions. An optional model-assisted pass turns those facts into prose. The extractor never depends on the model, so a missing API key degrades to a rule-based draft instead of failing silently.

Do you store my code?

No. The request is processed to produce a response and is not persisted. ContextForge has no database and no repository connector — it only reads what you paste into the form.

What happens if the AI is unavailable?

The endpoint fails closed. With no API key you get HTTP 503 with an explicit error code; if the upstream model call fails you get 502; if the fair-use quota is exhausted you get 429. You never receive a fabricated "AI-generated" result — a rule-based draft is always labelled source: rule-based.

Which agents are supported?

Cursor (.cursorrules), Claude Code (CLAUDE.md), OpenAI Codex (AGENTS.md), GitHub Copilot (.github/copilot-instructions.md), Gemini (GEMINI.md), and a generic AGENTS.md fallback that most tools read.

Blog

Practical notes for teams wiring coding agents. Honesty first: drafts need review before commit.

Definitional · Article
What an AI-ready context pack contains (2026)
A practical checklist for AGENTS.md, .cursorrules, CLAUDE.md and related files — grounded facts, forbidden paths, and honest limits.
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Engineering · Article
Deterministic extraction vs model guesswork
Why ContextForge extracts stack facts before any LLM prose pass, and how detected vs inferred labels keep drafts honest.
Read article →

Blog

What an AI-ready context pack contains (2026)

Deterministic extraction vs model guesswork