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 agentmods add commands/jovancoding/network-ai/statusgit clone --depth 1 https://github.com/Jovancoding/Network-AIWhat 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 | $0.00017 | $0.00146 |
| Opus 5 | $0.00009 | $0.00073 |
| Sonnet 5 | $0.00003 | $0.00029 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
status 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 2d 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
Give me a concise Network-AI swarm status report. Use the Network-AI MCP tools that are already loaded:
- Call
blackboard_listto get the current shared-state keys, thenblackboard_readon the most relevant ones (status, task, fsm keys). - Call
budget_statusfor the federated token budget. - Call
audit_queryfor the 10 most recent audit entries.
Summarize in three short sections: Blackboard, Budget, Recent activity. Flag anything unusual (denied permissions, budget near ceiling, stale state).
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.
- 2d ago First seen · 12 lines · 17 tokens per session scan A c6b95a1419b3
status is a command published in the GitHub repository Jovancoding/Network-AI (72 stars, last pushed 8d ago), licensed MIT. It adds 17 tokens to every session and 146 once invoked, about $0.0001 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 2026-08-30.
Other commands, from other repositories
dashboard-flow-auto
Toggle autonomous mode for a session's flow. Usage /dashboard:flow-auto.
dashboard-git-branches
List git branches for the current dir (current marked ). Runs locally, no LLM.
setup
Full onboarding for a new project — runs /doctor diagnostics, then injects the CLAUDE.md template + nav-ref instrumentation + Zustand store exposure so the plugin works without the user having to read documentation.
run-action
Execute a learned Maestro flow ("action") by name with optional -e KEY=VALUE parameters. Looks the flow up via packages/rn-dev-agent-core/dist/learned-actions.js (same inventory as /rn-dev-agent:list-learned-actions), then replays it via cdprunaction — auto-repair-aware orchestration with structured RunRecords (GH.
test-feature
Test a React Native feature on the running simulator/emulator. Verifies UI, user flows, and internal state. Generates a persistent Maestro test file.
nav-graph
Extract, inspect, and query the app navigation graph — a complete map of all screens and navigators.