Si

Sift – save AI tokens in Codex/Claude by summarizing command output

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Sift – save AI tokens in Codex/Claude by summarizing command output

I made a small skill/script for agentic coding workflows: https://github.com/panpeter/sift-skill The idea is simple: when a command like cargo test, pytest, npm test, or ./gradlew test prints a lot of output, that raw log often gets pulled into the context even though only a small part is actually useful. Sift runs the command, captures the full output, and asks a nested cheaper agent call to return only a compact summary. The goal is to keep the main thread smaller and easier to work with. In one command-heavy Codex workflow I used as a baseline, this looked like roughly ~45% lower total token cost. That number is an estimate, not a benchmark claim, and it will depend a lot on the workflow. It should help most when commands produce large noisy logs. It probably does very little for short commands. Would be interested in how others handle this problem in Codex / Claude / other coding-agent setups.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, agentic · Missing: mac, agents, macos
91%91% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
18%18% predicted probability of success on AppSumo, 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.

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