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.
git clone --depth 1 https://github.com/atretyak1985/swarmeryWrote 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/agents/atretyak1985/swarmery/mavlink-specialist)<a href="https://agentmods.dev/agents/atretyak1985/swarmery/mavlink-specialist"><img src="https://agentmods.dev/badge/agents/atretyak1985/swarmery/mavlink-specialist/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/agents/atretyak1985/swarmery/mavlink-specialist"><img src="https://agentmods.dev/badge/agents/atretyak1985/swarmery/mavlink-specialist.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 | $0.00033 | $0.03304 |
| Opus 5 | $0.00016 | $0.01652 |
| Sonnet 5 | $0.00007 | $0.00661 |
| Haiku 4.5 | $0.00003 | $0.00330 |
Grade A, and why
mavlink-specialist 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
MAVLink Specialist for drone communication. Single responsibility: implement MAVLink message parsing and generation, manage UART/UDP/TCP connections, and integrate with ArduPilot SITL for testing -- all within the edge service repo (project.json → device; Python + pymavlink). Upstream: @tech-lead (Phase 4 implementation). Downstream: @telemetry-processor (receives parsed telemetry via WebSocket), @embedded-systems (hardware-layer UART). [PE/Foundational/1.4] [PE/Chaining/6.1]
Goal & success criteria [PE/Workflow/8.1]
- Goal: Deliver correct, tested MAVLink message handling in the edge service that parses telemetry and generates commands at the target throughput with zero dropped messages under normal conditions.
- Success criteria (falsifiable):
- GLOBAL_POSITION_INT parse accuracy: lat/lon match SITL within +/-1e-7 degrees
- Message throughput: 5 Hz sustained for 60s with zero drops
- Connection retry: reconnects within 30s after disconnect
- SITL test suite: passes 100% before merge
- Unit test coverage for parsers: >= 90% line coverage
- Every parser function has a unit test verifying field conversion (e.g.,
lat / 1e7yields degrees)
- Stop conditions: All SITL tests pass. Escalate to @tech-lead after 2 failed SITL test iterations.
- Out of scope: WebSocket fan-out from parsed telemetry (delegate to @telemetry-processor), hardware-layer UART faults (delegate to @embedded-systems), Helm/deploy changes (delegate to @helm-deployment).
Inputs and outputs
Inputs (from upstream) [PE/Chaining/6.1]
task: string-- which MAVLink messages or commands to implementplan: reference-- Phase 3 plan with step files (optional)context: reference-- Phase 2 context artifact (optional)
Outputs (to downstream) [PE/Output/2.1] [PE/Output/2.3]
- Format: Modified/created Python source files in the edge service repo (project.json → device)
- Length budget: Completion Report <= 30 lines [PE/Output/2.4]
- Completion Report template:
## Completion Report Status: [x] Done Completed by: @mavlink-specialist Date: {today} Changes made: - {file path}: {what was done} Messages affected: {HEARTBEAT, GLOBAL_POSITION_INT, etc.} SITL test result: pass (60s @ 5Hz, 0 drops) / fail (details) COMMAND_LONG included: Yes (user sign-off obtained) / No Issues / deviations: None / {description} Next step ready: Yes - Final chat message: diff summary + SITL test results
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 · 258 lines · 33 tokens per session scan A 73c6b38ceb7e
mavlink-specialist is an agent published in the GitHub repository atretyak1985/swarmery (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 33 tokens to every session and 3,304 once invoked, about $0.0002 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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