ワンクリックで
proteus-fuzzing
Design calibrated Proteus fuzzing and differential probes that learn input behavior instead of spraying generic payloads.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Design calibrated Proteus fuzzing and differential probes that learn input behavior instead of spraying generic payloads.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Coordinate disciplined, continuous, professional vulnerability research with Proteus memory, campaigns, bounded delegation, validation gates, realistic exploitability, anti-slop controls, and report-grade decision discipline.
Operate as a secondary OpenCode-backed Proteus Chimera agent with coordinator-controlled scope, communication, snapshots, labs, and permissions.
Coordinate Proteus continuous vulnerability research with memory, campaigns, delegation, validation gates, and report-grade discipline.
Develop non-obvious Proteus exploit chains from primitives, side effects, state drift, and component coupling.
Compress Proteus campaign or round state into a concise checkpoint with facts, killed paths, pivots, and next moves.
Map a codebase for Proteus research through architecture, dataflow, trust boundaries, invariants, and high-ROI branch material.
| name | proteus-fuzzing |
| description | Design calibrated Proteus fuzzing and differential probes that learn input behavior instead of spraying generic payloads. |
| compatibility | opencode |
| metadata | {"source":"proteus"} |
Use this skill when a narrow surface needs input-reaction learning. Fuzzing here is an investigative loop: model the input, perturb one dimension at a time, observe behavior, update the model, and use what is learned to sharpen a branch.
Follow the Proteus base research contract. Do not create broad payload lists and call that research.
chaining.poc-exploit.web-intel.Required output:
{
"targetInvariant": "...",
"inputModel": {
"syntax": [],
"semantics": [],
"lifecycle": [],
"authorityContext": []
},
"oracles": [],
"probeFamilies": [
{
"family": "...",
"whatItCanTeach": "...",
"cases": [],
"expectedSafeBehavior": "...",
"killCondition": "..."
}
],
"observations": [],
"modelUpdates": [],
"candidatePrimitives": [],
"negativeControls": [],
"handoffs": [],
"contractSignature": {}
}