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 fastrevmd-lab/fwskillsshare --skill srx-mnhagit clone --depth 1 https://github.com/fastrevmd-lab/fwskillsshareWrote 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/fastrevmd-lab/fwskillsshare/srx-mnha)<a href="https://agentmods.dev/skills/fastrevmd-lab/fwskillsshare/srx-mnha"><img src="https://agentmods.dev/badge/skills/fastrevmd-lab/fwskillsshare/srx-mnha/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/fastrevmd-lab/fwskillsshare/srx-mnha"><img src="https://agentmods.dev/badge/skills/fastrevmd-lab/fwskillsshare/srx-mnha.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.07015 |
| Opus 5 | $0.00046 | $0.03508 |
| Sonnet 5 | $0.00018 | $0.01403 |
| Haiku 4.5 | $0.00009 | $0.00702 |
Grade A, and why
srx-mnha 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 499 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SRX Multi-Node High Availability (MNHA)
Overview
Multi-Node High Availability (MNHA) is Juniper SRX high availability built around independent SRX nodes that synchronize runtime state over routed HA links. Unlike chassis cluster, MNHA nodes do not become a single logical chassis. Each node keeps its own control plane, hostname, management, routing protocols, interface addressing, and node-specific configuration. Stateful firewall/NAT/IPsec runtime objects can still synchronize so traffic can survive a path or node failover when the design keeps routing, interfaces, policy, and HA state aligned.
Use MNHA as an L3-first HA design. Routing policy, BFD, link monitoring, service redundancy groups, and optional VIP behavior determine which node handles traffic. Avoid treating MNHA as a drop-in chassis-cluster clone; it solves different problems and has different failure modes.
Runtime intake
Use this skill only for MNHA-specific design and behavior. Use parsing-srx-configs for full-config extraction, srx-nat for general NAT, and srx-policy for general policy design.
Before acting, inspect the request, artifacts, and approved read-only evidence. If unresolved facts materially change safety, scope, correctness, confidence, or output, read references/runtime-intake.md. For each unresolved material fact whose catalog condition is true, invoke Claude AskUserQuestion or Codex request_user_input before continuing or issuing an open-ended request. Ask at most three single-select catalog questions per round. After each response, ask another round whenever any unresolved material catalog condition remains true; continue only when none remain. Do not repeat answered questions or show the full catalog. Without a native tool, present each selected catalog question with its 2-3 labeled choices and a free-text Other path in concise plain text; do not substitute a generic checklist. Never request secrets or unredacted customer data. Answers are context, not live-change approval; obtain separate explicit approval before configuration, commit, upgrade, reboot, delete, or failover.
What ships with it
11 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.
- agents/openai.yaml 196 B
- references/mnha-advanced-workflows.md 12 KB
- references/mnha-config-patterns.md 4.5 KB
- references/mnha-grid-model-field-notes.md 5.8 KB
- references/runtime-intake.md 6.7 KB
- references/source-dhcp-on-mnha-back-to-basics.md 2.0 KB
- references/source-hybrid-mnha-with-ebgp.md 2.1 KB
- references/source-index.md 953 B
- references/source-mnha-ipsec-and-multiple-routing-instances.md 2.3 KB
- references/source-multi-node-high-availability-basics.md 2.5 KB
- references/source-srx-from-chassis-cluster-to-mnha.md 2.2 KB
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.
- 9d ago First seen · 499 lines · 91 tokens per session scan A 187be2d66b0e
srx-mnha is a skill published in the GitHub repository fastrevmd-lab/fwskillsshare (8 stars, last pushed 12d ago), licensed MIT. It adds 91 tokens to every session and 7,015 once invoked, about $0.0005 per session on Opus 5. 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 2026-08-31.
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