hypothesis-driven-hunting
Skill9 starsChanged 4 months agoLicence not identifiedView on GitHub
Run a threat hunt as a falsifiable hypothesis about adversary behavior, not a keyword sweep — state before you query what you expect to see if it is true, what would refute it, and what "found nothing" actually proves, which is almost always far less than the hunter wants to claim. Use whenever someone is threat hunting or proposing to: "let's hunt for lateral movement", "go look for signs of compromise", "hunt for persistence", "we should proactively search for X", "did we find anything", "the hunt came back clean", "run an IOC sweep". Sibling of falsifiable-testing (a hunt that cannot fail found nothing) and detection-engineering (a confirmed behavior becomes a rule); it is the abductive loop pointed at an adversary. It does not write the detection rule or adjudicate a single hit in isolation (dual-use-behavior-adjudication), and never reads "no evidence found" as "no adversary present".
In this repository91 other files it gives its agents.
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- frontend-component-boundariesSkill
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- The first 60 shown.
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “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.
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