A CLI dropped this week that does something most AI tools refuse to do: force the model to admit when it’s not sure. Most tools chase bigger context windows or fancier prompts. This one attacks the actual failure mode, a model that sounds confident while being completely wrong.
A Redditor got tired of the same pattern. Feed a model one big task, get back a polished answer that falls apart on a second look. The fix wasn’t a smarter single prompt. It was breaking the task into a chain of smaller ones, each with its own goal and a checkpoint before moving forward.
So this builder shipped a small CLI that automates exactly that. Give it a task like “write a product strategy for a CLI tool” and it splits the work into phases, runs each phase as its own focused prompt, and only advances when that phase holds up. If a phase is weak, the tool re-prompts itself instead of dragging the weak output into the next step.
Here’s the twist, and it’s the part worth stealing even if you never touch the tool: the confidence gate. After every phase, the model scores its own output from 0 to 100. Below the threshold, it re-prompts itself with the specific gap it’s missing. Above it, it moves on. A model forced to say “I’m only 40% sure about this” catches its own weak reasoning instead of shipping it straight to you.
That single mechanic is doing more work than it looks like. Most prompt chains just pass output from step to step and hope quality holds up. This one adds a checkpoint that actively hunts for weak reasoning before it can contaminate the next phase. It’s the difference between a pipeline and a pipeline with a quality inspector standing at every junction.
The Mini-Workflow
- 🧩 Decompose the task into 4+ phases: understand, plan, execute, synthesize. Each phase gets one narrow objective, never two things at once.
- 📝 Run one prompt per phase, carrying context forward from the last phase plus an explicit “don’t do X yet” guardrail.
- ✅ Score confidence after each phase. Low score triggers a re-prompt with the gap named. High score moves the chain forward.
- 🔗 Synthesize the phase outputs into one final result, saved as a reusable “skill” you can run again later without rebuilding the chain from scratch.
The tool itself, called IKKF, runs locally on Mac, Linux, or Windows, and connects to whatever model you’ve already got: ChatGPT through the OpenAI API, Claude, Ollama, Groq, OpenRouter, or any OpenAI-compatible endpoint. One catch worth flagging before you get excited: it needs an actual API key. A ChatGPT Plus subscription alone doesn’t unlock one, so factor in the extra cost of API access if you want to run this against GPT models.
If you’ve used other prompt-chaining tools or agent frameworks, the difference here is scope. Most of those are built for developers wiring up multi-step agents in code. This is a CLI aimed at anyone who just wants a better answer to one hard task, without touching a line of Python.
Pro tip: You don’t need the CLI to use the idea. Next time you’re writing a big prompt, split it into phases by hand and add one line at the end of each: “Rate your confidence in this output from 0-100, and name the biggest gap.” That single line does most of the work the whole tool is built around, and it costs you nothing but a few extra words.
Second pro tip: The guardrail matters more than people think. Telling a model what NOT to do yet in each phase stops it from jumping ahead and half-solving step 3 while you’re still on step 1. Skip that line and the phases blur back into one messy prompt, and you’re right back where you started.
Third pro tip: Treat the confidence score as a diagnostic, not a vanity metric. A model that reports 90% on a phase it barely engaged with is a sign your objective for that phase was too vague. Tighten the phase goal until the score actually reflects effort.
I’ve run into this exact wall more times than I’d like to admit: a model nails the outline, then quietly botches the details because it never had to check its own work before handing it back. Forcing a self-graded checkpoint between steps is such an obvious fix that it’s a little annoying nobody made it standard practice sooner. The best part is you don’t have to wait for a tool to fix this for you.
If you want to try the phase-and-confidence-gate structure this week, you don’t need to install anything first. Pick your next big task, split it into four phases by hand, and add the confidence-score line after each one. Watch how often the model admits it’s only 60% sure, then actually fixes the gap instead of bluffing through it. That habit alone will change more answers than any prompt tweak you’ve tried this month.
Go check out the original Reddit thread for the full breakdown and the builder’s answers on the phase logic and confidence-gating details in the comments 🚀
I built a CLI that turns one vague task into a structured chain of sub-prompts with confidence checks — here’s the actual prompt structure it uses
by u/Minimum-Ad7274 in ChatGPTPromptGenius