My AI-Assisted Workflow

progx

My AI-Results

imiric

Why is everyone compelled to write one of these articles? Do they think that their workflow is so unique that they've unlocked the secret to harnessing the power of a pattern generator? Every single one of these reads like influencer vomit. My workflow hasn't changed since 2022: 1. Send some data. 2. Review response. 3. Fix response until I'm satisfied. 4. Goto 1.

gbrindisi

This is pretty much a spec driven workflow. I do similar, but my favorite step is the first: /rubberduck to discuss the problem with the agent, who is instructed by the command to help me frame and validate it. Hands down the most impactful piece of my workflow, because it helps me achieve the right clarity and I can use it also for non coding tasks. After which is the usual: write PRDs, specs, tasks and then build and then verify the output. I started with one the spec frameworks and eventually simplify everything to the bone. I do feel it’s working great but someday I fear a lot of this might still be too much productivity theater.

nDRDY

Here's mine: code to spec until I get stuck -> search Google for the answer -> scan the Gemini result instead of going to StackOverflow.

hansmayer

No kids, don´t put yourself through this suffering. If you have to invest so much deliberate effort to sort of make it work - while you still handle the most tenuous and boring parts yourself, then what is the point? Lets keep the LLM vendors to their word - they promised intelligent machines that would just work so well to the point of causing mass unemployment. Why on earth do we have to work around the LLMs to make them work? What is the point? Where is my nation of datacenter PhDs or a PocketPhd, depending on whose CEOs misleading statement one quotes?

zkmon

Congrats! You just rediscovered something called water-fall model.

slopinthebag

My workflow starts with dusting off my trusty spell book and checking which deities are currently listening and active. They only listen for so long, before I must pause for a few hours to allow them to return their gaze. I’m learning I need to be more deliberate in my spell casting, lest I exhaust their patience too quickly. I light the appropriate candles for focus, align my ritual circle, and open a fresh page for the day’s invocation. I polish my staff and prepare the inscription tools. I sketch out a loose intention on parchment, never too precise at first, just enough to give the spirits a direction. Then I begin the incantations, carefully chosen phrases spoken into the void until something answers back. Sometimes the reply is coherent, sometimes it is… enthusiastic in a way I did not ask for, but all responses are recorded for refinement. I keep a small set of favorite incantations that tend to calm the louder gods, though I still experiment when I’m feeling bold. Before committing anything to permanence, I perform a small divination to see if the current path is “stable.” The results are rarely definitive, but the ritual itself seems to keep things from collapsing immediately. Once a workable manifestation appears, I bind it with additional runes to keep it from drifting. If it behaves unpredictably, I perform a cleansing rite: repeating sections of the invocation with stricter wording until the spirit settles. There are also moments of silent bargaining, short offerings of clarity in exchange for fewer surprises later. When things truly misbehave, I consult older, more temperamental deities buried deeper in the book, though they are expensive to wake and rarely generous. Finally, I seal the result, store it in the grimoire, and extinguish the candles, hoping I won’t need to reopen that particular circle again too soon.

pydry

>What is AI actually good at? Implementation. What is it genuinely bad at? Figuring out what you actually want I've found it to be pretty bad at both. If what you're doing is quite cookie cutter though it can do a passable job of figuring out what you want.

Bossie

My workflow is also highly inspired by Matt's skills, but I'm leveraging Linear instead of Github. /grill-me (back-and-forth alignment with the LLM) --> /write-a-prd (creates project under an initative in Linear) --> /prd-to-issues (creates issues at the project level). I'm making use of the blockedBy utility when registering the issues. They land in the 'Ready for Agent' status. A scheduled project-orchestrator is then picking up issues with this status leveraging subagents. A HITL (Human in the loop) status is set on the ticket when anything needs my attention. I consider the code as the 'what', so I let the agent(s) update the issues with the HOW and WHY. All using Claude Code Max subscription. Some notes: - write-a-prd is knowledge compression and thus some important details occasionally get lost - The UX for the orchestrator flow is suboptimal. Waiting for this actually: https://github.com/mattpocock/sandcastle/issues/191#issuecom... - I might have to implement a simplify + review + security audit, call it a 'check', to fire at the end of the project. Could be in the form of an issue.

crustycoder

He's also missed a major step, which is to feed your skill into the LLM and ask it to critique it - after all, it's the LLM that's going to act on it, so asking it to assess first is kinda important. I've done that for his skills, here's the assessment: ========== Bottom line Against the agentskills.io guidance, they look more like workflow specs than polished agent skills. The largest gap is not correctness. It is skill design discipline: # stronger descriptions, # lighter defaults, # less mandatory process, # better degraded-mode handling, # clearer evidence that the skills were refined through trigger/output evals. Skill Score/10 write-a-prd 5.4 prd-to-issues 6.8 issues-to-tasks 6.0 code-review 7.6 final-audit 6.3 ========== LLM metaprogramming is extremely important, I've just finished a LLM-assisted design doc authoring session where the recommendations of the LLM are "Don't use a LLM for that part, it won't be reliable enough".

tim-projects

I automated a lot of this with a tool I wrote - https://github.com/tim-projects/tasks-ai It's not perfect by all means but it does the job and fast. My code quality and output increased from using it.

throwatdem12311

Congratulations you reinvented spec-kit.

lbreakjai

My workflow is quite similar, but it's leveraging Notion instead of markdown files. https://github.com/tessellate-digital/notion-agent-hive The main reason is we're already using Notion at work, and I wanted something where I could easily add/link to existing documents. Sample size of one, but I've noticed a considerable improvement after adding a "final review" step, going through the plan and looking at the whole code change, over a naive per-task "implement-review" cycle.

cg-enterprise

Did you compare your flow to superpowers/GSD?

fpauser

> What is AI actually good at? Implementation. AI is good in generating a lot of spaghetty code.

yanis_t

Spec-driven approach is fun. I wonder at which point of anytime at all we are going to commit only specs into the got repo, while the actual code can be generated. Obviously we’re not here yet because of price, context, and non-determinism, but it’s nice area to experiment with.

troupo

I just use /brainstorming from https://github.com/obra/superpowers/tree/main Then I tell it to write a high level plan. And then rum subagents to create detailed plans from each of the steps in the high-level one. All olans must include the what, the why, and the how. Works surprisingly well, especially for greenfield projects. You have to manually revie the code though. No amount of agentic code review will fix the idiocy LLMs routinely produce.

didibear77

This looks a lot like the [BMad Method]( https://github.com/bmad-code-org/BMAD-METHOD )

skydhash

> The single most valuable shift I made was treating every feature as a thinking problem first and an implementation problem second That’s pretty much the whole point of software engineering. Coding is easy, solving problems is hard and can be messy (communication errors and workarounds to some inevitable issue). If you’re familiar with the codebase, when you have a change request, you will probably get an insight on how to implement it. The hard thing is not to code it, but to recalibrate all the tradeoffs so that you don’t mess with existing features. That’s why SWE principles exists. To make this hard thing easier to do.

satisfice

This workflow is another example of a developer with contempt for testing. Yes there is iteration and review and output checking. In relatively low risk projects that is enough— but so is basic vibe coding. At some point in a serious project a responsible adult must ask the question: “How do I know this works well?” The developer himself is an unreliable judge of this. LLMs can’t judge, either. But anyone who seeks to judge, in a high stakes situation, must take time and thought to test deeply.