Re

Repogather – copy relevant files to clipboard for LLM coding workflows

Hacker News

Repogather – copy relevant files to clipboard for LLM coding workflows

Hey HN, I wanted to share a simple command line tool I made that has sped up and simplified my LLM assisted coding workflow. Whenever possible, I’ve been trying to use Claude as a first pass when implementing new features / changes. But I found that depending on the type of change I was making, I was spending a lot of thought finding and deciding which source files should be included in the prompt. The need to copy/paste each file individually also becomes a mild annoyance. First, I implemented `repogather --all` , which unintelligently copies all sources files in your repository to the clipboard (delimited by their relative filepaths). To my surprise, for less complex repositories, this alone is often completely workable for Claude — much better than pasting in the just the few files you are looking to update. But I never would have done it if I had to copy/paste everything individually. 200k is quite a lot of tokens! But as soon as the repository grows to a certain complexity level (even if it is under the input token limit), I’ve found that Claude can get confused by different unrelated parts / concepts across the code. It performs much better if you make an attempt to exclude logic that is irrelevant to your current change. So I implemented `repogather "<query here>"` , e.g. `repogather "only files related to authentication"` . This uses gpt-4o-mini with structured outputs to provide a relevance score for each source file (with automatic exclusions for .gitignore patterns, tests, configuration, and other manual exclusions with `--exclude <pattern>` ). gpt-4o-mini is so cheap and fast, that for my ~8 dev startup’s repo, it takes under 5 seconds and costs 3-4 cents (with appropriate exclusions). Plus, you get to watch the output stream while you wait which always feels fun. The retrieval isn’t always perfect the first time — but it is fast, which allows you to see what files it returned, and iterate quickly on your command. I’ve found this to be much more satisfying than embedding-search based solutions I’ve used, which seem to fail in pretty opaque ways. https://github.com/gr-b/repogather Let me know if it is useful to you! Always love to talk about how to better integrate LLMs into coding workflows.

Share card

Actual performance

65points
33comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, new, coding · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, soon · Missing: platform, intuitive, reviews
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Fi
Find relevant/irrelevant files from SHA1 sums50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find relevant/irrelevant files from SHA1 sums

Hacker News2
pd
pd-replicator – Copy a DataFrame to the clipboard with one click33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

pd-replicator – Copy a DataFrame to the clipboard with one click

Hacker News2
Al
Alpine.js Copy to Clipboard43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Alpine.js Copy to Clipboard

Hacker News2
Co
Copy any code with syntax highlighting to clipboard in OS X48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Copy any code with syntax highlighting to clipboard in OS X

Hacker News4
Co
Copy any code to clipboard in OS X, adding proper syntax highlighting53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Copy any code to clipboard in OS X, adding proper syntax highlighting

Hacker News55
Se
Search and copy symbols to clipboard (Notion Hack)29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Search and copy symbols to clipboard (Notion Hack)

Hacker News1
Co
Copy from tmux/nvim to clipboard over SSH63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Copy from tmux/nvim to clipboard over SSH

Hacker News38
Xc
Xclip-Multilevel clipboard for Xcode26%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Xclip-Multilevel clipboard for Xcode

Hacker News1
Slipboard
Slipboard44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Clipboard

Product Hunt+1
Quill
Quill64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Persistent clipboard for your Workflows

Product Hunt+129Productivity