suboss87/FDEOps

What is FDEOps?

FDEOps is a Node‑JS command‑line toolkit that packages a collection of “task skills” for forward‑deployed engineers – engineers who work directly with customers, iterating on requirements, architecture and code while the project is live. The package does not contain an AI model itself; instead it is meant to be called from an existing AI‑coding‑assistant (e.g. a ChatGPT‑style agent) and supplies the agent with structured, step‑by‑step instructions for common engineering activities such as discovery, scoping, design options, building, debugging, review, shipping and hand‑off.

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How it works

1. Installation – npx skills add suboss87/fdeops --skill <name> adds a single skill (e.g. build) or the coordinator skill fde to the user’s AI‑coding environment. The tool runs on Node 18+ and Git; no network calls are made by the CLI itself.
2. Customer record – When a project is ongoing, FDEOps creates a local Markdown folder (~/fde‑engagements/<customer>/.fde/) that stores decisions, evidence, and next steps. The coordinator (fde) reads this record to keep context across separate AI sessions.
3. Task skills – There are 35 small, self‑contained “skills” (e.g. discover, options, build, debug). Each skill knows what input it needs (meeting notes, code repo, test plan, etc.) and returns a concise, actionable output that the AI agent can present to the engineer or directly apply to code.
4. Coordinator (fde) – The fde skill can automatically select the appropriate task skill based on the current situation, orchestrating a workflow that moves from strategy → architecture → engineering.
5. No model inside – All AI calls are delegated to the user’s own LLM provider. This keeps data local and lets regulated teams use approved models.

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Who should use it?

• Forward‑deployed engineers, consultants, delivery teams who need a repeatable, documented process for turning vague customer requests into concrete, ship‑ready code.
• Teams that already employ an AI coding assistant and want a library of proven prompts/steps that the assistant can invoke without the engineer memorising CLI commands.
• Organizations that must keep customer data on‑premise; the CLI never contacts external services.

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What you get out of the box

Category	Example skills	What it does
Strategy	discover, who-decides, scope	Turns a brief into a problem statement, success metrics, and identifies decision‑makers.
Architecture	options, plan, integrate	Surveys the existing system, lists design alternatives, and drafts a small slice to test.
Engineering	build, debug, review, ship, handoff	Generates implementation steps, runs local verification, produces a review summary, and creates a hand‑off runbook.
Coordination	fde (coordinator), dashboard, debrief, switch‑clients	Keeps the local customer record, shows a read‑only “fieldbook” of next actions, and switches context between customers.

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Quick start example

# Add the coordinator skill to your AI agent
npx skills add suboss87/fdeops --skill fde

# In a chat with your AI assistant
@fde this is client01. Their support team reads incoming requests, checks internal docs, then assigns each request to another team. Help me prepare for the first meeting. Here is the brief: …

The assistant will run the appropriate discovery and scoping skills, update the local record, and return a ready‑to‑use meeting agenda.

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Limitations & considerations

• Only a prompt library – FDEOps does not perform code generation itself; it structures the work for an external LLM.
• Local‑only data – All records are plain Markdown files on the developer’s machine; you must manage backups and access control.
• Node & Git required – The CLI depends on a recent Node runtime and a Git checkout of the target codebase.

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Where to learn more

• Docs – docs/README.md, USAGE.md, skills-reference.md
• Demo – npx fdeops demo shows a fictional meeting turned into a fieldbook.
• Examples – examples/ contains end‑to‑end walkthroughs.
• Community – Issues and Discussions on GitHub for feature requests and real‑world use cases.

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TL;DR

FDEOps is a CLI‑based skill catalog that helps AI‑augmented engineers run a disciplined, customer‑focused workflow—from understanding the problem to shipping code—while keeping all project data local and under the team’s control.