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Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up.
Passed 17 of 22 test cases with the skill, and 16 of 22 without it.
3 fixed14 passed either way3 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I'm reviewing PR #402 where we refactor the session cache eviction logic. Can you analyze the potential blast radius beyond simple call-site searches? Please give me an analysis covering: 1. A summary of non-obvious behavior changes. 2. The key underlying safety assumption and a test or evidence proving whether it holds. 3. Confirmed risks with exact file/line paths, probability, and severity, alongside potential issues you investigated and cleared. 4. A minimal script or reproduction step to run before we merge.
| Checked | Without skill | With skill |
|---|---|---|
| Evaluates the certainty step level for the main safety fact. | Missed | Passed |
| Provides executable code or test output to verify the safety assumption. | Passed | Passed |
The instructions your model reads when the skill loads.
Find what a change breaks somewhere else, before it ships. Use for "blast radius of X", "what could this break", or reviewing a small diff you don't trust yet. Run it before design, not only before shipping, when a brief asserts something about existing code ("make endpoint X public"): the assertion is a hypothesis, and the consumer census is what decides the design.
Companion to how and why. how tells you what the code does. why tells you why it's shaped that way. Blast radius tells you what it breaks somewhere else.
Listing the callers is not the job. The agent can grep those in a second. The job is the breakage grep won't show you.
A blast-radius writeup that sounds right is worthless. It reads as convincing whether or not it's true. So don't hand back the writeup. Find the one or two facts the whole thing depends on and prove them by running code.
For each fact the change's safety depends on, get it as far down this list as is cheap, and say where it stopped.
file:line, or the library's own source.Step 4 is usually one small script that imports the same library the app ships and calls the exact function you're worried about.
why step 2 to pull the PR and commits.why. Cite a real file:line, a search that finds nothing is still an answer, and never make up a caller or an API.arena. Ask more than one model the same question and merge the answers. Different models catch different real bugs.file:line, how likely and how bad, and how to check. Paste the proof for the ones that matter.test cases passed, vs 16 / 22 without
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