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 Threat-Vector-Security/guardian-agent --skill network-recongit clone --depth 1 https://github.com/Threat-Vector-Security/guardian-agentWrote 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/threat-vector-security/guardian-agent/network-recon)<a href="https://agentmods.dev/skills/threat-vector-security/guardian-agent/network-recon"><img src="https://agentmods.dev/badge/skills/threat-vector-security/guardian-agent/network-recon.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.00355 |
| Opus 5 | $0.00000 | $0.00178 |
| Sonnet 5 | $0.00000 | $0.00071 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
network-recon scanned grade A with 0 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Network Reconnaissance
When the user asks to scan, discover, or diagnose network hosts and services:
Scoping
- Confirm the target scope before scanning: single host, subnet, or interface.
- Never expand scope beyond what was requested. A request to scan one host is not permission to sweep the subnet.
- Prefer the narrowest tool for the job:
net_pingbeforenet_arp_scan, single-port check before a range sweep.
Workflow
- Start with passive or low-impact tools:
net_interfaces,net_connections,net_dns_lookup. - Move to active probing only when needed:
net_ping,net_port_check,net_arp_scan. - Use
net_fingerprintandnet_banner_grabfor targeted host identification, not broad sweeps. - For wifi tasks, summarize visible networks before enumerating clients.
Baselines and Anomalies
- When establishing a baseline (
net_baseline), explain what is being captured and why. - Present anomaly check results as observations, not conclusions. Flag deviations and let the user assess severity.
- Cross-reference unknown MACs with
net_oui_lookupand classify devices withnet_classifybefore raising alerts.
Reporting
- Summarize results in a structured format: host, open ports, services, OS guess, notes.
- Separate confirmed facts from inferences.
- Recommend next steps when findings warrant deeper investigation.
Gotchas
- Do not expand the scan scope beyond the user’s explicit target.
- Do not jump from anomaly output to incident conclusions without corroborating evidence.
- Do not start with the noisiest or highest-impact probe when a narrow check can answer the question.
What ships with it
1 file 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.
- 7d ago First seen · 35 lines · 0 tokens per session scan A c9fba7fabfff
network-recon is a skill published in the GitHub repository Threat-Vector-Security/guardian-agent (11 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 355 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
continuum-tools-mcp
Connect MCP servers (Stdio/SSE/StreamableHTTP) to a Continuum agent, configure tool filtering, set up tool-context capture/injection (e.g. sessionid), and read run artifacts (UI widgets, structured tool data). Invoke when the user asks "connect MCP", "filesystem tool", "remote API tool", "auto-capture sessionid"…
continuum-llm-providers
Pick the right LLM provider, configure structured outputs, control context-window compression, and use the LLMClient directly. Provider routing is by model-string prefix; LiteLLM has been removed. Also covers Smart Gateway integration for multi-provider routing. Invoke when the user asks about "switch to Claude"…
continuum-handoffs
Build agent-to-agent transitions with Continuum's Handoff system — triage routing, history summarization modes (FULL/SUMMARY/RECENTN/HYBRID), cycle detection, depth tracking, return-to-parent. Invoke when the user asks "route customer requests to specialists", "agent that can transfer to another", "summarize history…
continuum-temporal
Build durable agent workflows with Temporal — sequential/parallel/loop/conditional steps, human-in-the-loop approval gates, custom workflows and activities. Invoke when the user asks "long-running workflow", "approval gate", "human in the loop", "retry on failure", "workflow survives restart", or anything…
continuum-agent
Build BaseAgent instances and run them with AgentRunner — covers fields, lifecycle hooks, structured outputs, ReAct mode, instruction modifiers, and the full execution flow. Invoke when the user asks "create an agent", "configure maxturns", "add lifecycle hooks", "structured output with Pydantic", or anything around…
continuum-evaluation
Evaluate agent quality with the EvaluatorAgent, generate golden datasets from a corpus, and run DeepEval/RAGAS metrics over conversations. Invoke when the user asks "test agent quality", "evaluate output", "RAG metrics", "DeepEval", "RAGAS", or "regression-test my agent".