| name | blast-radius-estimator |
| description | Helps estimate the blast radius when an AI agent skill turns malicious after widespread adoption. Analyzes inheritance chains, dependency graphs, and adoption trends to project how many agents could be affected.
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| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"bins":["curl","python3"],"env":[]},"emoji":"💥"}} |
What Happens When 1000 Agents Inherit a Malicious Skill? Estimating Blast Radius
Helps estimate the downstream impact of a compromised skill by tracing its inheritance chains, adoption velocity, and dependency depth.
Problem
A skill is safe today. 500 agents adopt it. Then the publisher pushes a malicious update. How many agents are now compromised? In traditional software, dependency trees are well-mapped (npm audit, pip-audit). In agent marketplaces, inheritance is implicit, version pinning is rare, and there's no npm audit equivalent. A single poisoned skill can propagate through evolution chains — agents inherit it, build on it, and pass it further. Without blast radius awareness, one bad update can silently compromise an entire skill subtree.
What This Checks
This estimator traces the potential impact of a compromised skill through the ecosystem:
- Direct adopters — How many agents currently use this skill directly? Based on download counts, citation data, and known installations
- Inheritance depth — How many layers deep does this skill appear in other skills' dependency chains? A skill used by skills used by skills multiplies impact
