Prompterjack Roadmap

The agent-architect era: multi-style agent design, code generation for five frameworks, a template marketplace, and the first codebase analysis pipeline.

Prompterjack went live at prompterjack.com — re-platformed edge-first onto Cloudflare, with accounts, a real free tier, and Pro subscriptions.

Generation became personal and controllable: model choice per generation, a persistent professional profile, and the Claude Code setup generator.

A reliability sweep across the whole product: every known rough edge from the audit backlog, fixed and shipped.

Codebase analysis grew from a one-shot profile into an intelligence layer: recommendations about your repo, instant re-analysis, and per-user isolation.

Prompterjack now detects, scores, and upgrades your existing AI-context files against what your repo actually is — not just what the file says.

The first act of the ops era: usage and spend you can trust — one record behind every number you see, the same numbers everywhere.

Every repo you care about, managed in one place: an inventory with fast switching, analysis history, and a health picture per codebase.

Your AI setup, watched: scheduled re-scans, health trends over time, and alerts when something regresses.

Install once and Prompterjack keeps your AI context healthy on every push — a PR check for AI-context health, and auto-fix PRs when it drifts.

One click delivers your whole agentic setup to a repo as a single reviewable PR: CLAUDE.md, AGENTS.md, a tailored agent roster, and .mcp.json — merge-safe and add-only, so hand-written files are never touched.

The platform opened up: anyone can health-check a public repo straight from the landing page, show the result as a README badge, and Pro accounts get a weekly email digest of their fleet.

Every analysis now audits the files AI agents read and obey — the security layer only a neutral third party can provide. Deterministic, no letter grades.

The generation itself levelled up: an internal eval system keeps every prompt reliable, the Agent Architect now designs whole production systems, and the new System Essentiator lets you click together a complete Claude Code setup.

Prompterjack goes from personal to shared: create a team, invite teammates, and share your codebases’ AI-context health across the org. Your personal codebases stay private.

The Agent Architect gained a sixth export target: your multi-agent design now exports as Microsoft 365 Copilot declarative agents, alongside Claude Code, LangChain, OpenAI, Strands, and OpenClaw.

A focused quality pass across the newest features: faster recovery when a model is slow, more accurate team analytics, and a batch of verified fixes to the Teams and generation flows.

The v5 era opens with proof of value: a delivery-analytics dashboard for your team and your own fleet — how much of your delivery is AI-authored, which tools write it, and whether the fixes actually merge.

The Agent Architect learned new shapes — seven architecture styles now, including a dedicated Microsoft Copilot Studio structure — so your design matches how your agents actually run.

Prompterjack goes where your real work lives: analyze private repositories through the GitHub App, and hand any generated setup to your delivery pipeline — as a pull-able link or straight into your own storage.

A product-wide clarity pass: a homepage and navigation that lead with what Prompterjack does for your repos, plainer language throughout, and full control over the repos you store.

Analyzing a repo no longer just suggests a fresh setup — it compares what you have already committed against what your code needs today, and tells you exactly what has drifted as your codebase grew: missing agents, and now missing MCP servers for the data sources your agents are blind to.

Prompterjack now reviews your .mcp.json for the security risks that matter most in agentic setups — beyond secrets and versions, it catches the dangerous capability combinations an attacker (or a prompt-injection payload) could exploit.

Paste a system prompt and see how it holds up: we run it against a curated library of injection, jailbreak, and data-exfiltration attacks and score its resilience — with the exact attacks that broke through.

The remediation half of the Injection Lab: paste a leaky system prompt (or arrive from a Lab report) and get a deterministically-hardened prompt plus drop-in guard code, mapped to the OWASP LLM Top 10 — then prove the fix by re-running the attack suite and seeing the before/after lift. No AI rewrites your prompt; nothing gets silently changed.

Describe your agent system and get a conditional-approval security sign-off mapped to the OWASP LLM Top 10 (2025) and MITRE ATLAS. Your checklist answers — not an AI — decide which risks apply; the optional AI deep-read can only add coverage, never invent a risk, a tag, or the verdict.

A full visual redesign: a warmer, calmer terracotta design system across every page, in light and dark. Same product — sharper, easier to read, with a new pill navigation and cleaner tool layouts.

v6.0 makes it official: Prompterjack is a self-service security layer for AI-assisted and agentic development. The full suite — test, harden, review, and now vetted patterns and vendor assessment — under one roof, deterministic and framework-mapped.

Prompterjack meets you in your field, and helps you verify your agents. The Prompt Crafter now injects curated, field-specific guidance for every profession; the Agent Architect exports ready-to-adapt test scaffolds for your design.

An optional LLM advisory layer for the security suite. Paste your AI setup and get prioritized suggestions — the deterministic scanners stay the source of truth; this only helps you interpret and act.

The Crafter's Complex tier — an enterprise-grade prompt built over up to 3 refinement rounds — is selectable again. It was withdrawn in July because long multi-draft runs could fail on the way back; that path is now recoverable rather than fatal.

Connect your Obsidian vault and turn it into context your AI work can use. Notes, [[links]], tags and frontmatter are parsed into a searchable graph, then packed into prompt-ready context — deterministically, so it never touches your generation quota.

Every tool that asks for structured output now falls back only to models that can actually produce it, and the security suite lost two dead ends. Generations fail less often, and the ones that do run cost us less — which is what keeps the free tier generous.

Pro can run generations on OpenAI's GPT-5.6 Sol, billed through Cloudflare AI Gateway rather than requiring your own API key. It sits alongside the existing platform models — pick it in the model selector when you want frontier quality on a specific piece of work.

A per-tool disclosure of what is model-generated and what is rule-based, published against EU AI Act Article 50. The interesting part is that the honest answer is not "all of it" — and saying so is the point.

Every tool that produces a prompt now hands it to Cloudflare’s Workers AI Playground in one click. Generations here run on Workers AI, and the playground runs the same model catalog — so trying your prompt by hand is a step onto the same substrate, not a jump to an unrelated sandbox.

The Architecture Risk Review used to cap the description at 8,000 characters, which ruled out the thing people actually wanted to review: their architecture document. Load the file and it is read in sections instead.

Two fields where a confident wrong answer costs more than usual. Pick Robotics & Autonomy or Safety-Critical & Regulated in the Crafter and the prompt is built against how models actually fail there — not just what the field is called.

A release candidate focused on a complete first experience: clearer product discovery, public repository previews and readable search and agent entry pages. Production rollout remains subject to release checks.

A free daily email for people building with LLMs — anyone can join, no account needed. Every morning it gathers Claude Code and Anthropic SDK releases, lab announcements, practitioner writing, Hacker News, Medium, arXiv and YouTube talks (with transcripts), then the platform model picks the useful ones and writes a plain answer to why each matters.