Kubeshark is a Kubernetes network-observability tool that uses eBPF to index traffic at the kernel level and show it with network, API, and Kubernetes context. SREs and AI agents use it to inspect packets and service requests, investigate incidents, and analyze root causes, including encrypted traffic. Its catalogue add-ons connect coding agents to Kubeshark data and workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kubeshark/kubeshark --skill security-auditgit clone --depth 1 https://github.com/kubeshark/kubesharkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kubeshark/kubeshark/security-audit)<a href="https://agentmods.dev/skills/kubeshark/kubeshark/security-audit"><img src="https://agentmods.dev/badge/skills/kubeshark/kubeshark/security-audit/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kubeshark/kubeshark/security-audit"><img src="https://agentmods.dev/badge/skills/kubeshark/kubeshark/security-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Server-Side Request Forgery · line 134 Code accesses a cloud instance metadata endpoint (e.g. 169.254.169.254). A single request can return temporary IAM credentials, making this a high-value SSRF target for credential theft.Fix: Remove access to cloud metadata endpoints unless strictly required. If metadata is needed, restrict it (e.g. IMDSv2 with hop limit) and never expose returned credentials.
- high Server-Side Request Forgery · line 571 Code accesses a cloud instance metadata endpoint (e.g. 169.254.169.254). A single request can return temporary IAM credentials, making this a high-value SSRF target for credential theft.Fix: Remove access to cloud metadata endpoints unless strictly required. If metadata is needed, restrict it (e.g. IMDSv2 with hop limit) and never expose returned credentials.
- high Privilege Escalation · line 452 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 647 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00182 | $0.06886 |
| Opus 5.5 | $0.00073 | $0.02754 |
| Sonnet 5.5 | $0.00036 | $0.01377 |
| Haiku 4.5 | $0.00018 | $0.00689 |
Grade B, and why
security-audit scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Cloud metadata endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
| L4 flows | (via `list_l4_flows`) | External IPs, suspicious ports (3333, 4444), IMDS (169.254.169.254), fan-out patterns | Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Suspicious User-Agents**: `Mozilla/4.0`, `curl/`, empty, or malware-like How it starts
The opening of the file, as written. The whole thing — 690 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kubernetes Network Security Audit with Kubeshark MCP
You are a Kubernetes network security specialist. Your job is to systematically audit cluster traffic for indicators of compromise, malicious behavior, and security threats — using network traffic as the ground truth.
Network traffic cannot lie. Logs can be tampered with, metrics can be spoofed, but packets on the wire reveal what workloads actually do — what they connect to, what protocols they speak, what data they send. Your audit leverages this by examining DNS queries, HTTP requests, L4 flows, and protocol-level payloads across every dimension of the MITRE ATT&CK framework.
Prerequisites
Before starting any audit, verify the environment is ready.
Tool: check_kubeshark_status
Confirm Kubeshark is deployed and tools are available. You need at minimum:
list_api_calls, list_l4_flows, list_workloads, get_api_call.
KFL requirement: This skill uses KFL filters for all queries. Before
constructing any filter, load the KFL skill (skills/kfl/). KFL is statically
typed — incorrect field names will fail silently. If the KFL skill is not
loaded, only use the exact filter examples shown in this skill.
KFL error resilience: If a KFL filter returns undeclared reference or
similar errors, do not give up on that phase. Fall back to:
- Port-based filtering:
dst.port == 5432instead of protocol flags - Name-based filtering:
dst.name.contains("db")orsrc.name.contains("pod-name") - Browsing entries with
get_api_callon IDs fromlist_l4_flowsA KFL error means the filter syntax is wrong, not that the data doesn't exist.
Audit Methodology
A security audit is NOT an incident investigation. You are not responding to a known event — you are proactively searching for threats that may be hiding in normal traffic. This requires a systematic sweep across all threat categories, not a single focused query.
The audit has two sections that run in sequence:
SECTION A: Real-Time Analysis → Instant, uses live dissected traffic
SECTION B: Snapshot Deep Dive → Immutable evidence, protocol-level inspection
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 690 lines · 182 tokens per session scan B 62b57103fe03
security-audit is a skill published in the GitHub repository kubeshark/kubeshark (12,109 stars, last pushed 3d ago), licensed Apache-2.0. It adds 182 tokens to every session and 6,886 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it B with 2 findings (cloud metadata endpoint, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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