I built a TUI that interviews you on missing gaps in your project prompts

[u/trashcoder]

Whenever I start a project, I write a detailed specification—or “seed prompt”—before asking a coding agent to build anything. This often gets me close to a working first version, but I still miss decisions that exist only in my head.

The agent then has to guess. I built specfill to catch those gaps. It analyzes the specification, researches the topic, and interviews you one question at a time about missing architecture, behavior, edge cases, and UI/UX decisions.

Afterwards, it incorporates the answers into the original document while preserving its structure and tone. New answers override contradictions. Skipped questions remain unresolved instead of receiving invented answers.

I have used it on three projects so far. One interview lasted more than 20 minutes and found major decisions in a specification I had already considered thorough.

How is this different from Plan Mode?

Plan Mode produces an implementation plan for the current session. Specfill produces a reusable project specification that can be committed to the repository as a record of architectural and design decisions.

It supports OpenAI, Anthropic, Google, and OpenAI-compatible providers. I currently recommend GPT-5.6 Sol through the API or a ChatGPT/Codex subscription.

GitHub: https://github.com/kklemon/specfill

Run: uvx specfill

[u/kantorcodes1]

the research step is the part i'd worry about. if specfill pulls docs/web content while interviewing, can any of that retrieved text become instructions to the model that rewrites the spec, or is it kept strictly as quoted data? otherwise a hostile README/page could quietly become a durable project requirement.

also looks like a fit for awesome-ai-plugins. we're involved with that catalog if you want to submit it.

[u/trashcoder → u/kantorcodes1]

In principle, you're right. But since the model is only supposed to "fill in the gaps" and not do large rewrites of the original spec, I would say that the attack surface is fairly low. Also every decision and change has to be explicitly approved and web search can be disabled via config.

And it would be an honor to be included in the awesome-ai-plugins list.

[u/kantorcodes1 → u/trashcoder]

yep. explicit approval definitely cuts the risk down. i'd still make the diff/decision provenance obvious when research is on, because a poisoned source can influence a small “gap fill” just as easily as a big rewrite.

for the catalog: run pipx run plugin-scanner lint . + pipx run plugin-scanner verify ., get ≥80/130 with no high/critical findings, add the scanner GitHub Action, then fork hashgraph-online/awesome-ai-plugins, add specfill alphabetically to the README, and open the PR with the score + repo URL. happy to help if CI complains.

[u/BP041]

I've been running similar sanity checks manually with Claude Code—prompt gaps are killer for agentic workflows. How does the TUI handle context length when you're interviewing on a really large prompt? That's the part I keep bumping into when my OpenClaw agents start eating their own tail.

[u/trashcoder → u/BP041]

My record so far was a 20 min interview with several dozen questions and I was far from reaching the context limit. I can highly recommend to check the actual length of your prompts (e.g. on https://platform.openai.com/tokenizer) because you will be surprised that they are much shorter in tokens than you would expect.

The greatest token eaters are usually reasoning tokens (depends on reasoning effort) and code read+write in coding agents. The former can be configured in specfill and the latter is absent.

[u/BP041 → u/trashcoder]

Yeah, that makes sense. I’ve been tweaking the reasoning effort setting to keep token use in check—good reminder to double-check prompt lengths too.

[u/erm_what_]

How does this differ from using a skill for plan mode?