Agent Skills
› H-mmer/pentest-agents
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learn
GitHub记录平台漏洞报告反馈并更新知识库。解析参数后调用工具记录响应、同步大脑数据并展示洞察。通过分类处理（接受/重复等）提取经验，转化为未来狩猎规则或报告优化策略，形成持续学习闭环。
Trigger Scenarios
用户输入 /learn 命令及报告ID
需要记录平台对漏洞报告的反馈结果
Install
npx skills add H-mmer/pentest-agents --skill learn -g -y
SKILL.md
Frontmatter
{
"name": "learn",
"description": "Record a platform response and update learning. Usage: \/learn <report_id> <status> [--bounty 500] [--vuln-type XSS]",
"disable-model-invocation": false
}
Record platform response: $ARGUMENTS
- Parse arguments and run:
uv run python3 $CLAUDE_PROJECT_DIR/tools/response_tracker.py log $ARGUMENTS - Also update brain:
uv run python3 $CLAUDE_PROJECT_DIR/tools/brain.py log "Report response: $ARGUMENTS" - Sync to global brain:
uv run python3 $CLAUDE_PROJECT_DIR/tools/global_brain.py sync-from-local - Show updated insights:
uv run python3 $CLAUDE_PROJECT_DIR/tools/response_tracker.py insights
Top-Tier Learning Loop
Convert every platform response into a future hunting rule.
- If accepted: record the decisive proof artifact, impact framing, asset type, vuln variant, bounty tier, and why triage agreed.
- If duplicate: record the duplicated primitive and which uniqueness signal was missing.
- If N/A: record the exact sentence or policy clause that killed it.
- If informative: record the missing chain or business impact required to make it payable.
- If severity changed: record the evidence that moved it up or down.
End with one concrete update: a brain pattern, a never-submit rule, a report wording change, or a target ranking adjustment.
Version History
- 41d49b6 Current 2026-07-24 11:57
