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 automateyournetwork/netclaw --skill pyats-parallel-opsgit clone --depth 1 https://github.com/automateyournetwork/netclawWrote 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/automateyournetwork/netclaw/pyats-parallel-ops)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/pyats-parallel-ops"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/pyats-parallel-ops/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/automateyournetwork/netclaw/pyats-parallel-ops"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/pyats-parallel-ops.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 | $0.00065 | $0.03838 |
| Opus 5 | $0.00032 | $0.01919 |
| Sonnet 5 | $0.00013 | $0.00768 |
| Haiku 4.5 | $0.00006 | $0.00384 |
Grade A, and why
pyats-parallel-ops 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Fleet Operations
When to Use
- Fleet-wide health checks across all devices in the testbed
- Mass configuration audits (collect running configs from every device)
- Network-wide routing table snapshots for baseline or comparison
- Pre/post change validation across all affected devices simultaneously
- Any operation where running sequentially on 10+ devices would be too slow
How pCall Works in OpenClaw
In OpenClaw, parallel execution (pCall) is achieved by listing multiple exec commands in a single response. The agent runtime dispatches them concurrently and collects all results before proceeding.
Key Principles
- Group by role or site -- Organize devices into logical groups (core, distribution, access, WAN, DC) before dispatching
- Run operations concurrently -- List one MCP call per device; they execute in parallel
- Failure isolation -- If one device times out or errors, the other results are still collected
- Result aggregation -- Collect all results, then produce a unified fleet report
- Severity sorting -- Sort findings from CRITICAL to HEALTHY so the worst problems surface first
pCall Pattern
To run the same command on multiple devices in parallel, list the calls together:
# Device 1
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R1","command":"show version"}'
# Device 2
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"R2","command":"show version"}'
# Device 3
PYATS_TESTBED_PATH=$PYATS_TESTBED_PATH python3 $MCP_CALL "${PYATS_PYTHON:-python3} -u $PYATS_MCP_SCRIPT" pyats_run_show_command '{"device_name":"SW1","command":"show version"}'
All three commands execute concurrently. Results arrive independently and are aggregated by the agent.
Step 0: Discover the Fleet
Always start by listing all devices in the testbed so you know what to operate on:
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 · 321 lines · 65 tokens per session scan A b1a535a1bbaa
pyats-parallel-ops is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It adds 65 tokens to every session and 3,838 once invoked, about $0.0003 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 2026-09-03.
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