HIPAA Safe Harbor Coverage Audit
maziyarpanahi/openmed
Checks OpenMed de-identified clinical text against the 18 HIPAA Safe Harbor identifier categories and reports gaps and residual re-identification risk.
Agent skill
Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .claude/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .claude/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicoreType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .agents/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .agents/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .cursor/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .cursor/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/implementing-microsegmentation-with-guardicore--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .gemini/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .gemini/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicoreInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .github/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .github/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills implementing-microsegmentation-with-guardicore --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/implementing-microsegmentation-with-guardicore .opencode/skills/implementing-microsegmentation-with-guardicore && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "implementing-microsegmentation-with-guardicore" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/implementing-microsegmentation-with-guardicore into .opencode/skills/implementing-microsegmentation-with-guardicore/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "implementing-microsegmentation-with-guardicore", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
implementing-microsegmentation-with-guardicoreImplements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…
Implementing Microsegmentation With Guardicore is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud. Use when blocking lateral movement in a data center or when PCI DSS/HIPAA compliance requires validated network segmentation.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).
It sits in Legal & Compliance, covering Healthcare and finance regulation and Red teaming and adversary simulation. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
curlpython3kubectlFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
management.guardicore.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
GC_API_TOKENGC_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Implementing Microsegmentation With Guardicore loads about 3.1k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 102 tokens; SKILL.md has 562 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
sudo ./gc-agent-installer.sh \Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 562 words, ~3,111 tokens.
.claude/skills/implementing-microsegmentation-with-guardicore/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.Do not use for perimeter-only security (use traditional firewalls), for environments with fewer than 50 workloads where VLANs/security groups suffice, or when network team lacks capacity for ongoing policy management.
Install agents to collect process-level network communication data.
# Linux agent installation
curl -sSL https://management.guardicore.com/api/v3.0/agents/download/linux \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-o gc-agent-installer.sh
chmod +x gc-agent-installer.sh
sudo ./gc-agent-installer.sh \
--management-url=https://management.guardicore.com \
--site-id=datacenter-east \
--label="web-tier"
# Windows agent installation (PowerShell)
# Invoke-WebRequest -Uri "https://management.guardicore.com/api/v3.0/agents/download/windows" `
# -Headers @{"Authorization"="Bearer $GC_API_TOKEN"} `
# -OutFile gc-agent-installer.exe
# Start-Process -FilePath .\gc-agent-installer.exe `
# -ArgumentList "--management-url=https://management.guardicore.com","--site-id=datacenter-east" `
# -Wait
# Kubernetes DaemonSet deployment
cat > gc-daemonset.yaml << 'EOF'
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: guardicore-agent
namespace: guardicore
spec:
selector:
matchLabels:
app: gc-agent
template:
metadata:
labels:
app: gc-agent
spec:
hostNetwork: true
hostPID: true
containers:
- name: gc-agent
image: guardicore/agent:latest
securityContext:
privileged: true
env:
- name: GC_MANAGEMENT_URL
value: "https://management.guardicore.com"
- name: GC_API_KEY
valueFrom:
secretKeyRef:
name: gc-credentials
key: api-key
volumeMounts:
- mountPath: /host
name: host-root
volumes:
- name: host-root
hostPath:
path: /
EOF
kubectl apply -f gc-daemonset.yaml
# Verify agent enrollment
curl -s "https://management.guardicore.com/api/v3.0/agents?status=active" \
-H "Authorization: Bearer ${GC_API_TOKEN}" | python3 -m json.toolUse Guardicore Reveal to discover and visualize application communication patterns.
# Query discovered application flows via API
curl -s "https://management.guardicore.com/api/v3.0/connections" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{
"time_range": {"from": "2026-02-17T00:00:00Z", "to": "2026-02-24T00:00:00Z"},
"filter": {
"source_label": "web-tier",
"destination_label": "app-tier"
},
"aggregation": "process",
"limit": 1000
}' | python3 -m json.tool
# Export application dependency map
curl -s "https://management.guardicore.com/api/v3.0/maps/export" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{
"format": "json",
"labels": ["web-tier", "app-tier", "db-tier"],
"time_range": "7d"
}' -o app-dependency-map.json
# Typical discovery findings:
# web-tier -> app-tier: TCP 8080, 8443 (expected)
# app-tier -> db-tier: TCP 5432, 3306 (expected)
# web-tier -> db-tier: TCP 5432 (UNEXPECTED - should be blocked)
# app-tier -> internet: TCP 443 (verify if needed)Define labels and create ring-fence policies around applications.
# Create labels for application tiers
curl -X POST "https://management.guardicore.com/api/v3.0/labels" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"name": "PCI-CDE",
"description": "Cardholder Data Environment workloads",
"criteria": {"ip_ranges": ["10.10.0.0/16"]},
"color": "#FF0000"
}'
# Create segmentation policy: Allow web-to-app communication
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"name": "Web-to-App Allowed",
"action": "ALLOW",
"priority": 100,
"source": {"labels": ["web-tier"]},
"destination": {"labels": ["app-tier"]},
"services": [
{"protocol": "TCP", "port": 8080},
{"protocol": "TCP", "port": 8443}
],
"log": true,
"enabled": true,
"section": "application-segmentation"
}'
# Create deny policy: Block web-to-database direct access
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"name": "Block Web-to-DB Direct",
"action": "DENY",
"priority": 200,
"source": {"labels": ["web-tier"]},
"destination": {"labels": ["db-tier"]},
"services": [{"protocol": "TCP", "port_range": "1-65535"}],
"log": true,
"alert": true,
"enabled": true
}'
# Create ring-fence policy for PCI CDE
curl -X POST "https://management.guardicore.com/api/v3.0/policies" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-H "Content-Type: application/json" \
-d '{
"name": "PCI CDE Ring Fence",
"action": "DENY",
"priority": 50,
"source": {"labels": ["!PCI-CDE"]},
"destination": {"labels": ["PCI-CDE"]},
"services": [{"protocol": "TCP", "port_range": "1-65535"}],
"log": true,
"alert": true,
"enabled": true
}'Simulate policy enforcement without blocking traffic.
# Enable reveal mode (log-only) for new policies
curl -X PATCH "https://management.guardicore.com/api/v3.0/policies/POLICY_ID" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{"enforcement_mode": "REVEAL"}'
# Check what would be blocked in reveal mode
curl -s "https://management.guardicore.com/api/v3.0/violations" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{
"time_range": "24h",
"policy_id": "POLICY_ID",
"limit": 100
}' | python3 -c "
import json, sys
data = json.load(sys.stdin)
for v in data.get('violations', []):
print(f\"{v['source_ip']}:{v['source_process']} -> {v['dest_ip']}:{v['dest_port']} [{v['action']}]\")
"
# After validation, switch to enforcement
curl -X PATCH "https://management.guardicore.com/api/v3.0/policies/POLICY_ID" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{"enforcement_mode": "ENFORCE"}'Set up alerting and continuous monitoring for segmentation violations.
# Configure SIEM integration for policy violations
curl -X POST "https://management.guardicore.com/api/v3.0/integrations/syslog" \
-H "Authorization: Bearer ${GC_API_TOKEN}" \
-d '{
"name": "Splunk SIEM",
"host": "splunk-syslog.company.com",
"port": 514,
"protocol": "TCP",
"format": "CEF",
"events": ["policy_violation", "agent_status", "deception_alert"]
}'
# Splunk query for microsegmentation violations
# index=guardicore sourcetype=guardicore:policy
# | where action="DENY" AND enforcement_mode="ENFORCE"
# | stats count by src_ip, dst_ip, dst_port, policy_name
# | sort -count| Term | Definition |
|---|---|
| Microsegmentation | Network security technique creating granular security zones around individual workloads or applications to control east-west traffic |
| Reveal Mode | Guardicore's simulation mode that logs policy decisions without enforcing them, allowing validation before blocking |
| Ring-Fence Policy | Isolation policy that restricts all traffic into or out of a defined group of assets (e.g., PCI CDE) |
| Application Dependency Map | Visual representation of discovered network communication patterns between workloads showing processes, ports, and protocols |
| East-West Traffic | Network traffic flowing laterally between workloads within a data center, as opposed to north-south traffic crossing the perimeter |
| Process-Level Visibility | Guardicore's ability to identify which process on a workload initiated or received a network connection |
Context: An e-commerce company must isolate its Cardholder Data Environment (CDE) from the rest of the corporate network for PCI DSS compliance. The CDE spans 200 servers across on-prem and AWS.
Approach:
Pitfalls: Agent deployment on legacy systems (Windows Server 2012) may require manual installation. Ring-fence policies must account for management traffic (monitoring, patching, backup). Start with broad allow rules and progressively tighten. Application owners must validate dependency maps before enforcement.
Microsegmentation Deployment Report
==================================================
Organization: E-Commerce Corp
Report Date: 2026-02-23
AGENT DEPLOYMENT:
Total workloads: 500
Agents installed: 487 (97.4%)
Agents active: 482 (98.9%)
Agentless (flow logs): 13
POLICY COVERAGE:
Total policies: 45
Allow rules: 38
Deny rules: 7
Reveal mode: 3
Enforced: 42
TRAFFIC ANALYSIS (7 days):
Total flows observed: 2,456,789
Flows matching allow: 2,441,234 (99.4%)
Flows matching deny: 15,555 (0.6%)
Unclassified flows: 0
PCI CDE ISOLATION:
CDE workloads: 200
Ring-fence violations: 0 (last 30 days)
Authorized CDE entry points: 4
Lateral movement paths blocked: 95%© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (scripts, references) in skills/implementing-microsegmentation-with-guardicore of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Implementing Microsegmentation With Guardicore next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Implementing Microsegmentation With Guardicore this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | |
| HIPAA Safe Harbor Coverage Auditmaziyarpanahi/openmed | 5.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| HIPAA Pre-Deployment Compliance Checkmaziyarpanahi/openmed | 5.5k | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Hipaa ComplianceSushegaad/Claude-Skills-Governance-Risk-and-Compliance | 942 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| ISO Standards Readiness EvidenceK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.6k | Automated safety check: Notes | MIT | |
| Fda Consultant Specialistdavila7/claude-code-templates | 32k | 1 repos | ~2.7k | Automated safety check: Pass | MIT |
maziyarpanahi/openmed
Checks OpenMed de-identified clinical text against the 18 HIPAA Safe Harbor identifier categories and reports gaps and residual re-identification risk.
maziyarpanahi/openmed
Walks a data pipeline against the HIPAA Privacy and Security Rule checklist and produces a gap report before it processes patient data.
Sushegaad/Claude-Skills-Governance-Risk-and-Compliance
Expert HIPAA compliance assistant for healthcare and software contexts.
K-Dense-AI/scientific-agent-skills
Organizes scope, controlled documents, risk files and traceability into draft evidence for human review against ISO 13485, 14971, 17025 and 15189.
davila7/claude-code-templates
Senior FDA consultant and specialist for medical device companies including HIPAA compliance and requirement management.
mlunato47/claude-grc-plugin
Senior GRC analyst expertise across 18 compliance frameworks — NIST 800-53, FedRAMP (Rev5 + 20x/CR26, KSIs, VDR/VER, Certification Classes A–D), DoD/DoW Impact Levels (IL2–IL6, DISA Cloud SRG), ITAR…
mukul975/Anthropic-Cybersecurity-Skills
Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
mukul975/Anthropic-Cybersecurity-Skills
Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
mukul975/Anthropic-Cybersecurity-Skills
Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.
mukul975/Anthropic-Cybersecurity-Skills
Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.
Categories
Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs…. Implementing Microsegmentation With Guardicore is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Implements microsegmentation with Akamai Guardicore Segmentation to map application dependencies, visualize east-west traffic flows, and create granular, least-privilege network policies across VMs, containers, bare metal, and cloud.
Implementing Microsegmentation With Guardicore fits situations like: blocking lateral movement in a data center; PCI DSS/HIPAA compliance requires validated network segmentation.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a claude-code`. Or copy the skill folder (skills/implementing-microsegmentation-with-guardicore in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/implementing-microsegmentation-with-guardicore in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a codex`. Or copy the skill folder (skills/implementing-microsegmentation-with-guardicore in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/implementing-microsegmentation-with-guardicore in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-microsegmentation-with-guardicore -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/implementing-microsegmentation-with-guardicore, .gemini/skills/implementing-microsegmentation-with-guardicore, .github/skills/implementing-microsegmentation-with-guardicore and .opencode/skills/implementing-microsegmentation-with-guardicore in your project.
Going by SKILL.md and its folder, Implementing Microsegmentation With Guardicore needs Python for the scripts in its folder, the command-line tools its instructions call (curl, python3 and kubectl) and credentials named GC_API_TOKEN and GC_API_KEY. Our summary lists: Python 3; A credential in GC_API_TOKEN; A credential in GC_API_KEY.
SKILL.md names 1 domain. In commands or code: management.guardicore.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Implementing Microsegmentation With Guardicore is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 422 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Implementing Microsegmentation With Guardicore: HIPAA Safe Harbor Coverage Audit (maziyarpanahi/openmed, 5.5k stars), HIPAA Pre-Deployment Compliance Check (maziyarpanahi/openmed, 5.5k stars), Hipaa Compliance (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 942 stars) and ISO Standards Readiness Evidence (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 skills in this directory. The repository was last updated on August 31, 2026.
Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.