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 skills/pantani/ableton-mind/device-schema-extractionnpx skills add Pantani/ableton-mind --skill device-schema-extractiongit clone --depth 1 https://github.com/Pantani/ableton-mindWrote 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/pantani/ableton-mind/device-schema-extraction)<a href="https://agentmods.dev/skills/pantani/ableton-mind/device-schema-extraction"><img src="https://agentmods.dev/badge/skills/pantani/ableton-mind/device-schema-extraction.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 | $0.00028 | $0.00258 |
| Opus 5 | $0.00014 | $0.00129 |
| Sonnet 5 | $0.00006 | $0.00052 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
device-schema-extraction 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 6d 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
Device Schema Extraction
Use this skill when populating src/knowledge/devices, indexing packs or changing scripts/extract-device-schemas.mjs.
Sources
- Bridge introspection of device.parameters for name, min, max, default and automation flags.
- Default.adv parsing for saved default state.
- Ableton manuals and LOM docs for behavior notes.
- Manual curation for units, nonlinear curves, macro targets and useful descriptions.
Workflow
- Extract or introspect raw parameters.
- Normalize slug, id, category, vendor and Live version metadata.
- Fill min, max, default, unit, automatable, modulatable and concise description.
- Mark source and completeness.
- Validate uniqueness of ids, names and indexes.
- Run recipe and tool checks that depend on the schema.
Quality Rules
Descriptions must be short, English and useful to an LLM. Do not invent ranges. Use unit=curve for nonlinear 0..1 controls and explain audible behavior. Keep runtime-discoverable facts out of static knowledge unless they are slow-changing and useful.
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.
- 6d ago First seen · 29 lines · 28 tokens per session scan A 0be6785c6d95
device-schema-extraction is a skill published in the GitHub repository Pantani/ableton-mind (5 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 258 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 2026-08-31.
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