Borrowing it
Nothing to install: this file belongs to zarfld/presonus-studiolive-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zarfld/presonus-studiolive-mcp/master/.github/Skills/routing-reverse-engineering/SKILL.mdgit clone --depth 1 https://github.com/zarfld/presonus-studiolive-mcpWrote 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/zarfld/presonus-studiolive-mcp/routing-reverse-engineering)<a href="https://agentmods.dev/skills/zarfld/presonus-studiolive-mcp/routing-reverse-engineering"><img src="https://agentmods.dev/badge/skills/zarfld/presonus-studiolive-mcp/routing-reverse-engineering/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/zarfld/presonus-studiolive-mcp/routing-reverse-engineering"><img src="https://agentmods.dev/badge/skills/zarfld/presonus-studiolive-mcp/routing-reverse-engineering.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 | $0.00000 | $0.01038 |
| Opus 5.5 | $0.00000 | $0.00415 |
| Sonnet 5.5 | $0.00000 | $0.00208 |
| Haiku 4.5 | $0.00000 | $0.00104 |
Grade A, and why
routing-reverse-engineering 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 1mo 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Routing Reverse Engineering
Purpose
Use this skill to implement or verify local input routing, AVB/stagebox routing, output patch routing, aux/mix routing, and semantic mapping of raw route indices.
This is a high-risk area. A sound-engineer agent cannot act correctly unless it knows what physical or AVB source feeds each channel and what output path receives each mix.
Applies to
get_input_routingvalidate_avb_routingvalidate_output_routing- routing graph/resource tools
- input-list validation tools
- stagebox / AVB tools
- adapter route parsing
- source index mapping
Required questions
For each routing capability, answer:
- What raw adapter data is used?
- What does each raw index mean?
- Is the mapping observed, inferred, or unknown?
- Can the tool distinguish local analog input from AVB/stagebox input?
- Can it identify wrong patching?
- Can it report confidence per route?
- Does the result match mixer UI / UC Surface / audible behavior?
- Is there HIL evidence for the exact model and firmware?
Required workflow
- Inventory existing routing tools and adapter data.
- Identify exposed stubs or
probe_requiredpaths. - Build a raw-data map:
- raw field name,
- observed values,
- semantic interpretation,
- evidence.
- Create or update HIL probes for unknowns.
- Implement semantic mapping only where evidence exists.
- For unknown mappings, return explicit uncertainty.
- Add golden fixtures from captured device state.
- Add unit tests for mapping logic.
- Add integration tests for MCP response shape.
- Update capability matrix and release checklist.
Required routing evidence table
Every routing change must produce this table:
| Route type | Raw source | Raw value | Semantic meaning | Evidence | Confidence |
|---|---|---|---|---|---|
| Local input | <field> |
0 |
Local Input 1 |
<fixture/probe> |
observed |
| AVB receive | <field> |
? |
unknown | none | probe_required |
Required MCP response behavior
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.
- 1mo ago First seen · 150 lines · 0 tokens per session scan A b1facdd2f442
routing-reverse-engineering is a skill published in the GitHub repository zarfld/presonus-studiolive-mcp (1 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,038 tokens. 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 2026-08-31.
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