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 parsing-firepower-configsgit 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/parsing-firepower-configs)<a href="https://agentmods.dev/skills/fastrevmd-lab/fwskillsshare/parsing-firepower-configs"><img src="https://agentmods.dev/badge/skills/fastrevmd-lab/fwskillsshare/parsing-firepower-configs/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/parsing-firepower-configs"><img src="https://agentmods.dev/badge/skills/fastrevmd-lab/fwskillsshare/parsing-firepower-configs.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00126 | $0.05487 |
| Opus 5 | $0.00063 | $0.02743 |
| Sonnet 5 | $0.00025 | $0.01097 |
| Haiku 4.5 | $0.00013 | $0.00549 |
Grade A, and why
parsing-firepower-configs 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 11d 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 — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parsing Cisco Firepower (FMC / FDM) Exports
Overview
Use this skill to parse Cisco Secure Firewall (Firepower) FMC and FDM management exports into the shared vendor-neutral firewall intermediate schema. It focuses on JSON from the FMC or FDM REST API, including access control policies, prefilter policies, NAT policies, security zones, intrusion policies, file policies, network and service objects, and application filters.
This parser owns FMC and FDM JSON exports. For ASA-style LINA running-config text (such as access-list, nameif, object network), hand off to parsing-cisco-configs instead.
Scope and routing
Use only for Cisco Secure Firewall (Firepower) FMC and FDM REST API JSON exports. Hand off ASA-style LINA text configs (access-list, nameif, object network) to parsing-cisco-configs; FortiOS to parsing-fortinet-configs; PAN-OS to parsing-palo-configs; Junos to parsing-srx-configs. Downstream consumers are the audit, conversion, and diff skills.
Runtime intake
Before starting the workflow, inspect the request, supplied artifacts, and
available approved read-only evidence. If unresolved facts could materially
change safety, scope, correctness, confidence, or the requested 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. Treat intake answers as task context, not approval for a live change; obtain separate explicit approval before configuration, commit, upgrade, reboot, delete, or failover actions.
What ships with it
9 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 240 B
- README.md 2.3 KB
- references/config-format.md 17 KB
- references/example-sample-parse.md 11 KB
- references/fixture-expected-output.json 7.6 KB
- references/fixture-minimal-input.md 8.3 KB
- references/intermediate-schema.md 20 KB
- references/parsing-patterns.md 3.6 KB
- references/runtime-intake.md 3.8 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.
- 11d ago First seen · 419 lines · 126 tokens per session scan A 20fdbd7aad4a
parsing-firepower-configs is a skill published in the GitHub repository fastrevmd-lab/fwskillsshare (8 stars, last pushed 14d ago), licensed MIT. It adds 126 tokens to every session and 5,487 once invoked, about $0.0006 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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