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proteus-fuzzing
Design calibrated Proteus fuzzing and differential probes that learn input behavior instead of spraying generic payloads.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Design calibrated Proteus fuzzing and differential probes that learn input behavior instead of spraying generic payloads.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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": {}
}