What is skill poisoning?
A complete starting point: what gets poisoned, how a skill gains influence, and which boundaries determine the outcome.
Explore 32 focused guides on poisoned skills, package review, execution boundaries, research interpretation, and recovery.
A complete starting point: what gets poisoned, how a skill gains influence, and which boundaries determine the outcome.
Read the package as a map of possible influence: metadata, instructions, references, scripts, assets, and dependencies.
How to reason about skill text that asks for more than the user’s task—and why enforcement must survive a bad interpretation.
Risk can start before the main skill instructions load. Review descriptions, retrieval, and the planner’s choice of package.
A skill’s supporting files can introduce instructions, destinations, or content that the entry file does not reveal.
Inspect the code path separately from the instruction path. A skill can introduce software behavior as well as natural-language guidance.
How to preserve the meaning of a review when package content, dependencies, or the execution environment changes.
Some skills bundle learned artifacts. Reviewing their instructions and scripts may not reveal the behavior encoded inside a model.
A workflow can create a risk that is difficult to see when each skill is reviewed in isolation.
An actionable map of influence, assets, actors, permissions, and recovery for one real skill workflow.
Design a permission profile that allows the task to work while limiting what a poisoned skill could reach.
A practical sequence for deciding what to approve, what evidence to retain, and when to keep a skill out of a sensitive workflow.
A concrete review baseline across package content, authority, execution, updates, and incident readiness.
Measure the complete path from loaded content to observable effect, while keeping evaluations isolated from production.
A guide to interpreting attack success rates, datasets, defenses, and benchmark claims without turning one result into a universal conclusion.
A review matrix from repository admission to evolution, with observable questions at every stage.
Practical authoring choices that help downstream reviewers understand scope, permissions, dependencies, and changes.
Contain the package, preserve evidence, distinguish attempts from effects, and recover from a reviewed state.
Why a reusable rule needs its own review, even when the task that produced it appeared successful.
Review the entire selection chain instead of treating skill metadata as harmless catalog text.
A guide to reviewing examples and templates that a coding agent may copy into a real workflow.
Source overlap, false alarms, and evaluation choices can change what a headline detection score means.
A research librarian’s checklist for changing titles, revised taxonomies, and conference status.
Separate vulnerable packages, confirmed malicious behavior, and deliberately assembled test collections.
Review installation requests and human handoffs as carefully as the actions an agent executes itself.
Learn to read skipped files, parser failures, truncated content, and mismatched package inventories.
An agent can finish the visible task while steering recommendations, recipients, or decisions away from the user’s goal.
Follow evidence, state, and authority across a skill chain when no single file explains the final action.
Read detection research in terms of observed signals, benchmark conditions, and deployment limits.
How to write a review result that does not confuse a heuristic, a trace, an attempted action, and impact.
Trace the authority of persistent files that connect multiple skills after chat history is cleared.
Inspect the entire package and its imports when a useful result could hide a separate side effect.
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