Packs

1 pack

AI-first Git CLI automation — atomic AI commits, co-change relations, autonomous git health agent, pre-tool hook safety, and workspace initialization

2 skills · pack

Results for “atom”

9 skills

performing-threat-emulation-with-atomic-red-team

Executes Atomic Red Team tests for MITRE ATT&CK technique validation using the atomic-operator Python framework. Loads test definitions from YAML atomics, runs attack simulations, and validates detection coverage.

24.6k · bundle

performing-purple-team-atomic-testing

Executes Atomic Red Team tests mapped to MITRE ATT&CK techniques, performs coverage gap analysis, and runs detection validation loops to measure blue team visibility.

24.6k · bundle

Guides blue-team threat hunting and detection engineering with hypothesis-driven workflows, Sigma/YARA rule creation, SIEM query design, and validation using Atomic Red Team in authorized environments.

12.8k · bundle

Session-end memory garbage collector. Promotes valuable L2 session facts to L1 atomic memory, wipes L2, and rotates oversized audit logs. Run at end of session to prevent context inflation and storage bloat.

More results

implementing-siem-use-cases-for-detection

Design, implement, test, and maintain SIEM detection rules mapped to MITRE ATT&CK across Splunk, Elastic, and Sentinel platforms.

24.6k · bundle

performing-false-positive-reduction-in-siem

Systematically reduce SIEM false positives through rule tuning, threshold adjustment, correlation refinement, and threat intelligence enrichment to combat alert fatigue.

24.6k · bundle

performing-purple-team-exercise

Coordinates purple team exercises by running MITRE ATT&CK-mapped attack scenarios with real-time detection testing and collaborative gap remediation.

24.6k · bundle

mapping-mitre-attack-techniques

Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization.

24.6k · bundle

Designer's eye QA: finds visual inconsistency, spacing issues, hierarchy problems, AI slop patterns, and slow interactions — then fixes them. Iteratively fixes issues in source code, committing each fix atomically and re-verifying with before/after screenshots. For plan-mode design review (before implementation), use /plan-design-review. Use when asked to "audit the design", "visual QA", "check if it looks good", or "design polish". Proactively suggest when the user mentions visual inconsistencies or wants to polish the look of a live site. (gstack)