Summarizer

Hook Aggressiveness Concerns

Criticism that blocking all curl/wget for 56KB snapshots is excessive when many API calls return minimal data; author acknowledged and removed

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While some users find utility in hooks that force models to search through temporary files rather than overloading the context with raw logs, others argue that blocking standard tools like curl for tiny API responses is unnecessarily aggressive. Critics highlight that extreme data compression—such as condensing git commits into tiny snapshots—places too much faith in the model’s ability to write perfect extraction scripts, often resulting in the loss of critical information. These concerns regarding reliability and the practical "noise" of blocked commands ultimately prompted the author to remove the feature, acknowledging that the efficiency gains did not justify the potential for data loss.

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> For example, if you’re working with a tool that dumps a lot of logged information into context I've set up a hook that blocks directly running certain common tools and instead tells Claude to pipe the output to a temporary file and search that for relevant info. There's still some noise where it tries to run the tool once, gets blocked, then runs it the right way. But it's better than before.
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Not really because it reliably greps or searches the file for relevant info. So far I haven't seen it ever load the whole file. It might be more efficient for the main thread to have a subagent do it but probably at a significant slowdown penalty when all I'm doing is linting or running tests. So this is probably a judgement call depending on the situation.
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The hooks seem too aggressive. Blocking all curl/wget/WebFetch and funneling everything through the sandbox for 56 KB snapshots sounds great, but not for curl api.example.com/health returning 200 bytes. Compressing 153 git commits to 107 bytes means the LLM has to write the perfect extraction script before it can see the data. So if it writes a `git log --oneline | wc -l` when you needed specific commit messages, that information is gone. The benchmarks assume the model always writes the right summarization code, which in practice it doesn't.
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Agreed. I removed it.