check-impact
Skill2 starsChanged 8 days agoMITView on GitHub
Finds what a change could break somewhere else before it ships, beyond the diff, and proves the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', reviewing a small diff you don't trust, or a brief that asserts something about existing code ('make X public', 'X already handles Y') before you design against that assertion.
In this repository18 other files it gives its agents.
- approve-planSkill
- context-packSkill
- design-forgeSkill
- deslopSkill
- diagnose-bugSkill
- falsifySkill
- forgeSkill
- gap-analysisSkill
- interrogateSkill
- make-fixtureSkill
- micro-loopSkill
- phased-loopSkill
- pick-tierSkill
- ship-planSkill
- simple-loopSkill
- taste-testSkill
- unslopSkill
- wait-whatSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 30
Read the change. The diff, the symbols it adds, changes, and deletes, and what it now does differently, including the part the diff doesn't …
- Distinctive: line 44
Write it through the unslop skill if you have it, cite real code, and strip anything private before it goes anywhere public.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Blast radius
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.
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.
Don't trust your own writeup
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.
How sure are you
For each fact the change's safety depends on, get it as far down this list as is cheap, and say where it stopped.
- You said so. Worthless on its own.
- You pointed at the line. A real
file:line, or the library's own source. - You showed the bad case can't happen. You walked the failure step by step and it doesn't reach.
- You ran it. A script or test that calls the real code and fails loud if you're wrong.
- You reproduced it in the running app.
Step 4 is usually one small script that imports the same library the app ships and calls the exact function you're worried about.
Steps
- Read the change. The diff, the symbols it adds, changes, and deletes, and what it now does differently, including the part the diff doesn't spell out. When there's a PR, pull it and its commits with the GitHub CLI.
- Find the one fact it's safe because of. Most changes that look risky are safe because of a single fact, like "this call only drops already-dead cache entries and does nothing else". Find that fact. If it holds, most risky cases are cleared at once. Spend your time here, not on a long list of maybes.
- Look where grep stops. Read the source of the library you call, and check its pinned version and any local patch. Work out when things run: microtasks, unmount and teardown, Solid versus React. Follow what a symbol search misses: the JSON an API returns, a DB column, a wire format, another language reading the same bytes, a feature flag, code three hops downstream.
- Be honest about each risk. Give it a real chance of happening and a real cost if it does. Keep the risks you confirmed. List the ones you checked and cleared separately. Cite a real
file:line. A search that finds nothing is still an answer. Never make up a caller or an API. - Prove the one fact. Write a script or test that runs the real code, run it, and paste what happened.
What to hand back
- What it does. What changed, including the part that isn't obvious.
- The one fact it's safe because of. State it, say which step you got it to, and show the proof. If you couldn't prove it, write unproven.
- Risks. Each names how it breaks, the
file:line, how likely and how bad, and how to check. Paste the proof for the ones that matter. - Cleared. What you checked and why it's fine.
- Before you merge. The cheapest test or repro that catches the real bug, including the script you wrote.
Write it through the unslop skill if you have it, cite real code, and strip anything private before it goes anywhere public.
Reply: the writeup above, with the one safety fact either proven or marked unproven.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

