Meshtastic-Android is an Android and Compose Desktop app for communicating through open-source mesh radios, which relay messages between nearby devices instead of relying on a conventional network. It is used with Meshtastic devices for messaging, node and channel discovery, location features, telemetry, and device management.
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/meshtastic/meshtastic-android/speckit.optimize.learngit clone --depth 1 https://github.com/meshtastic/Meshtastic-AndroidWrote 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/commands/meshtastic/meshtastic-android/speckit.optimize.learn)<a href="https://agentmods.dev/commands/meshtastic/meshtastic-android/speckit.optimize.learn"><img src="https://agentmods.dev/badge/commands/meshtastic/meshtastic-android/speckit.optimize.learn.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.02704 |
| Opus 5 | $0.00006 | $0.01352 |
| Sonnet 5 | $0.00002 | $0.00541 |
| Haiku 4.5 | $0.00001 | $0.00270 |
Grade A, and why
speckit.optimize.learn 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 today.
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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- today First seen · 288 lines · 12 tokens per session scan A 980e70586d1c
speckit.optimize.learn is a command published in the GitHub repository meshtastic/Meshtastic-Android (1,827 stars, last pushed today), licensed GPL-3.0. It adds 12 tokens to every session and 2,704 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 2026-09-04.
Other commands, from other repositories
learn
Extract patterns from current session into reusable knowledge.
instinct-export
Export learned instincts for sharing with others.
instinct-import
Import instincts from an exported file.
instinct-status
Show learned instincts with confidence scores.
app-learn
Mine a shipped app's conventions into the living House Knowledge Base — adds net-new learnings to knowledge/.md, harvests its failures into knowledge/failure-corpus.md, and flags conflicts for human decision.
app-context
Create a deterministic provenance manifest before an agent starts work.