Context hygiene for Claude Code. Caps verbose tool output and dedupes same-session re-reads so the model sees signal, not noise. Anthropic measures 29% quality lift from cleaner context. Proof: 62.6% median tool-output savings on a locked 20-task benchmark. MIT.
Hey PH. I'm Anuj, solo indie dev. Built Sipcode because I kept watching Claude Code re-read the same files 6-8 times per session and re-print 4,000-line npm install logs into its context. Each unnecessary token in the window pushes signal out and makes the next answer worse. That is the reliability problem I built it to fix.
It is a PreToolUse hook for Claude Code. Caps verbose output (git log, npm install, grep, tsc), dedupes same-session re-reads of unchanged files, exposes 15 MCP tools so Claude can read its own context-hygiene stats. Anthropic's own research: cleaner context lifts quality 29% and cuts agent errors 40%. That is the mechanism Sipcode targets.
Tokens saved are the PROOF the context got cleaner. Locked 20-task benchmark: 62.6% median tool-output savings, $67.43 per corpus run, reproducible on any machine. The benchmark task list is checked into the repo.
Honest disclosure that became the launch story: last week my drift tool said 624,940 tokens wasted in a single session. My proxy --stats credited only 7,553 saved. 83x undercount, my own tool lying to me. Root cause was mid-session installs leaving the first half of the session uncached. Shipped v1.6.15 with Verified Warm-Fill 24h later, drift now reads "no drift detected." Shipped v1.6.16 today with cache-defer and grep-cap fixes. Three releases in nine days.
MIT, zero network calls in normal use (privacy test fails the build if anyone imports node:http in src/). Happy to answer anything technical, especially the Warm-Fill correctness proof or the benchmark methodology.
If Sipcode saves you a session, a star on the repo at github.com/Anuj7411/sipcode would mean a lot to a solo project trying to find the people who would actually benefit from this.
Closing out my launch day soon, and I want to say thank you while it is still live.
The best part was never going to be the ranking, it was the comments. Valeria, David, and Art went straight at the dedup internals, the exact edge cases I obsess over: content-hash versus mtime, subagent context branches, LF and BOM canonicalization, uncapped output on demand. Those are the questions I most wanted someone to ask.
Gal, Harini, Rahul, and Jostin named the problem better than my own copy did, context bloat quietly poisoning a long session and a bigger window not fixing it. Florent and Viacheslav, thank you for engaging early. Patrick and Divv, thank you for the kind words on the build and the video, and for the honest feedback I am already acting on.
I built this solo because I was burning through Claude Code usage and the bill was my only feedback loop. Having people who feel the same problem show up today meant more than the number next to the name.
I am reading every comment for the rest of the day. Keep them coming.
Watching Claude Code re-read the same file six times and burn through its own context has become a daily annoyance, so putting a cap on that is genuinely useful. The 62% reduction in tool-output usage is wild. Congrats on the launch, Anuj!
Watching Claude Code re-read the same file 6 times and blow up its own context is a daily annoyance, so capping that is genuinely useful. The 62% tool-output savings number is wild. Congrats on the launch Anuj!
The same-session re-read dedup is the part I'd test first — re-reading unchanged files 6-8x is exactly what quietly poisons a long Claude Code session. How do you decide a file is "unchanged" between reads: mtime, content hash, or git state, and does that still hold when a subagent reads the same file in its own context branch? Caps on npm/tsc output make sense too, but can the model pull the full uncapped log on demand when it actually needs it?
Congrats on the launch, Anuj! As someone who lives in Claude Code all day (I've built a whole stack of custom skills around it), context bloat from verbose tool output is a very real pain, so a tool that caps it and dedupes same-session re-reads is solving something I actually feel.
Also have to call out the launch video. It's genuinely one of the best I've seen on PH, and the music gave me a wave of RPG nostalgia. Already chatted with you about it. Starring this and trying it on my setup today. 🌟
Claude Code rereading the same files and dumping huge logs into context is painfully familiar. I like that Sipcode tackles the boring cleanup layer instead of pretending a bigger context window fixes everything.
The re-read dedup looks like a clear win! QQ - when you inject a head_limit on a grep Claude ran without one, does the model see a "truncated, N more matches exist" marker? Does it read the capped list as the full set? Overall, very well done!
The dogfooding story sells this more than any benchmark — discovering your own drift tool read 624,940 tokens wasted while --stats credited 7,553 saved, then root-causing it to uncached mid-session installs and shipping Warm-Fill in 24h. Most launches would've quietly buried that. And the 38% duplicate-Read finding finally names that "why does this session feel sluggish" sensation I could never explain.
One question on the dedup: you canonicalize LF and BOM before the byte comparison. For files where whitespace carries meaning — Python, Makefiles, YAML — can that normalization ever flatten a real change into a false no-op, or is it strictly newline/BOM and never touches interior whitespace?
Claude Code users know how quickly context gets polluted with logs, repetitive outputs, and tool noise 😅 The idea of treating context as a limited resource rather than an infinite one really resonates. Curious... what was the most surprising source of context bloat you discovered while building Sipcode?
Congrats on the launch! Keeping Claude Code context clean is a very real pain point for anyone building with AI coding tools. I like the focus on sharper answers instead of just longer context. How are you deciding what should stay in context versus what should be summarized or dropped?
Context bloat is my #1 frustration with Claude Code in long sessions. You watch it re-read the same files and re-print npm install walls of text and by the end of a complex session the answers are noticeably worse. The 40% agent error reduction stat is the one that got my attention - quality lift is nice but errors are the thing that actually breaks workflows. The PreToolUse hook approach is smart because it intercepts before the context gets polluted rather than trying to clean up after. Installing this today. Does it handle situations where Claude Code genuinely needs to re-read a file because it changed, or does it dedupe those too?
Great minimal video you have I liked it but would have been more interesting with a voiceover!
The context window management problem in Claude Code is real. Long sessions accumulate dead weight fast, old tool outputs, abandoned approaches, redundant file reads, and once the context gets bloated the model starts hedging more and the answers get muddier. Curious whether Sipcode is doing something principled to decide what to prune (like deprioritizing failed attempts or stale file state) or whether it's more of a manual curation layer where you're telling it what to keep. Also wondering if there's any handling for cases where something that looked like a dead end earlier in the session turns out to be relevant again.
Hey, congrats!
A couple of questions.
Have you measured the quality performance somehow? I mean, the speed/quality on certain tasks.
Also - is it configurable be Claude to "disable" it if needed, if it things that the hook over-stripped the content?
Thanks!
About Sipcode on Product Hunt
“Keep Claude Code's context clean for sharper answers”
Sipcode launched on Product Hunt on June 23rd, 2026 and earned 135 upvotes and 30 comments, placing #12 on the daily leaderboard. Context hygiene for Claude Code. Caps verbose tool output and dedupes same-session re-reads so the model sees signal, not noise. Anthropic measures 29% quality lift from cleaner context. Proof: 62.6% median tool-output savings on a locked 20-task benchmark. MIT.
Sipcode was featured in Open Source (68.5k followers), Developer Tools (514.5k followers), Artificial Intelligence (471.8k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 211.5k products, making this a competitive space to launch in.
Who hunted Sipcode?
Sipcode was hunted by Anuj ojha. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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