u/FunNewspaper5161

I kept running into a gap with AI coding tools: they can produce a lot of code quickly, but the architecture review step is still easy to skip. I’m building ArcForge, an open-source set of portable Agent Skills distributed through the Skills CLI, to make that checkpoint repeatable.

The current package has three focused skills:

- system-architecture-harness: turns requirements into evidence-backed production architecture, with decisions, trade-offs, risks, validation, and implementation slices.

- ai-agent-system-architecture: reviews tool boundaries, memory/model routing, budgets, evaluations, safety, latency, and rollout gates for LLM/RAG and multi-agent systems.

- architecture-review-gate: adversarial review of RFCs, ADRs, diagrams, migrations, and readiness plans, ending in findings, blockers, a score/verdict, and approval conditions.

Install it with:

npx skills add d4rkninja/arcforge

Source: https://github.com/d4rkNinja/arcforge

Skills listing: https://skills.sh/d4rkninja/arcforge

This is my project, and I’d rather learn where the workflow is weak than pretend it catches everything. For people who build or maintain CLI/dev-tool workflows: what should an architecture review command ask for or refuse to approve? If you find it useful, a GitHub star would help other contributors discover it.

I’m building Ledgerly as an open-source alternative for households and small workspaces that want shared finance history to stay understandable.

The useful distinction is between fixing a mistake and pretending the old report never changed. The latest implementation adds:

- immutable review checkpoints for a reporting period

- visible post-review change markers

- cumulative deltas for the reviewed period

- exact before/after drill-downs

- preserved history across re-review generations

- separate occurrence/reporting dates and audit timestamps

It is designed so a personal user can correct an entry, while a shared workspace can still see what changed and when.

The merged implementation is here: https://github.com/d4rkNinja/ledgerly-app/pull/7

Project: https://github.com/d4rkNinja/ledgerly-app

I’d appreciate feedback from people who use open-source finance tools or build audit/history features: what should be visible in the default report, and what belongs behind the drill-down? If it looks useful, a star helps the project get in front of the right contributors.

I’m building Ledgerly for households and workspaces where finance history needs to be understandable, not just editable.

The latest feature set came from a real failure mode: someone backdates or corrects a transaction after a month has already been reviewed. The new workflow now:

- creates immutable review checkpoints with civil-date and timezone evidence

- shows cumulative changes to a reviewed period

- adds visible post-review markers

- opens an exact before/after drill-down

- preserves earlier history across re-review generations

- keeps personal and shared-workspace actions scoped differently

The goal is not to lock people out of fixing mistakes. It’s to make “what changed after I looked?” answerable in a few seconds.

Repo: https://github.com/d4rkNinja/ledgerly-app

I’m looking for practical feedback on the UX and data model, especially from people who share finances or build workflow/audit tools. If you try it and it earns a place in your toolkit, a star is appreciated.

I’m building two MIT-licensed projects, with most of my current focus going into INFYNON CLI.

1. INFYNON CLI

A security-first control layer for AI-driven development. It can check package risk before installation, audit dependencies, trace repository decisions and context, build and replay API workflows, and manage coding tasks across Codex, Claude, Gemini, or other agents.

Install: npm i -g infynon

Repo: https://github.com/d4rkNinja/infynon-cli

2. Ledgerly

A shared-finance platform for personal, family, and office-style workspaces. It covers transactions, accounts, budgets, goals, roles and permissions, expense claims and approvals, reports, CSV export, and audit history across web and Android.

Repo: https://github.com/d4rkNinja/ledgerly-app

INFYNON is the more mature project and the priority right now. Ledgerly is earlier and does not have a hosted demo or formal release yet.

For developers using multiple AI coding agents: which control is most useful to you—package safety, persistent repository context, API-flow validation, or task handoffs?

Disclosure: I’m the author of both projects.

I’m building INFYNON, a CLI control layer for AI-driven development.

Install:

npm i -g infynon

The new orchestration flow lets one main workflow create and manage multiple AI subagent tasks across different workspaces.

Example:

- Gemini reviews frontend code

- Gemini checks i18n grammar

- Codex reviews CLI bugs

- Claude can coordinate or handle another task

Each task can have its own agent, model, workspace, goal, context, constraints, and done condition.

The goal is simple:

use the right model for the right job, but keep the whole workflow controlled and traceable from the CLI.

Website: https://cli.infynon.com