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/blackboardgit 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 | $0.00016 | $0.00135 |
| Opus 5 | $0.00008 | $0.00068 |
| Sonnet 5 | $0.00003 | $0.00027 |
| Haiku 4.5 | $0.00002 | $0.00014 |
Grade A, and why
blackboard 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
Work with the Network-AI shared blackboard using the loaded MCP tools.
Arguments given: $ARGUMENTS
- If no arguments: call
blackboard_listand show all keys with their owners. - If one argument (a key): call
blackboard_readand show the value plus metadata. - If two arguments (key + value): call
blackboard_writewithagent_id: "claude-code", then confirm the write by reading it back.
Remember every write is identity-verified, namespace-scoped, and audit-logged.
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 · 14 lines · 16 tokens per session scan A d507ab9f1778
blackboard is a command published in the GitHub repository Jovancoding/Network-AI (72 stars, last pushed 8d ago), licensed MIT. It adds 16 tokens to every session and 135 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.