Build integrated IS/BS/CF financial workbooks in Excel.
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Use for pip CVE scanning and auto-patching via OSV.dev API.
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See how to installReview the instructions the agent will receive before you install.
Autonomer Security Agent, der alle installierten pip-Pakete gegen die OSV.dev Vulnerability Database scannt und bei kritischen CVEs automatisch patched.
scripts/security-cve-scan.py)Standort: $OPENAMER_HOME\scripts\security-cve-scan.py
Funktionsweise:
pip list --format=json/v1/querybatch) — bis zu 500 Pakete pro RequestCRITICAL) oder hohen (HIGH) CVEs: automatisches pip install --upgrade.security-cve/last-report.json.security-cve/state.jsonState-Verzeichnis: $OPENAMER_HOME\.security-cve\
state.json – bekannte CVEs, Scan-Statscve-scan.log – detailliertes Loglast-report.json – letzter Scan-ReportAuto-Patching:
MAX_PATCHES_PER_RUN)SKIP_PACKAGES konfigurierbarExit-Codes:
0 – Erfolg (keine kritischen CVEs gefunden oder alle gepatched)1 – Kritische CVEs gefunden, die nicht automatisch patcht werden konnten130 – Abbruch durch BenutzerDer Cron-Job läuft im OpenAmer-Cron-System und führt das Script automatisch aus.
Schedule: 0 */6 * * * (alle 6 Stunden)
Script-Pfad: scripts/security-cve-scan.py
cd /c/Users/damir/AppData/Local/openamer-laptop
python3 scripts/security-cve-scan.py
python3 scripts/security-cve-scan.py --json
cat /c/Users/damir/AppData/Local/openamer-laptop/.security-cve/cve-scan.log
cat /c/Users/damir/AppData/Local/openamer-laptop/.security-cve/last-report.json
POST https://api.osv.dev/v1/querybatchPyPI{"queries": [{"package": {"name": "...", "ecosystem": "PyPI"}, "version": "..."}]}| Problem | Lösung |
|---|---|
| OSV.dev API rate-limited (429) | Automatisches Retry mit exponential backoff |
| pip kann nicht gepatched werden | In SKIP_PACKAGES konfiguriert |
| Scan zu langsam | Reduziere MAX_PATCHES_PER_RUN im Script |
| Log voll | Log leeren: > /c/Users/damir/AppData/Local/openamer-laptop/.security-cve/cve-scan.log |
Der Security Agent harmoniert mit:
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Build integrated IS/BS/CF financial workbooks in Excel.
The English summary is being prepared. Showing the original description.
Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.
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Use for A/B experiments on OpenAmer configs and skills.
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Roleplay a hostile user to find and triage UX pain points.
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Agent mesh: master/worker nodes, HTTP delegation, heartbeat.
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Use for agentic commerce payments tasks. Candidate synthesized from trend radar; awaiting trial promotion.
The English summary is being prepared. Showing the original description.