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Package or technical metadata updated
View technical comparison →Kaizen Strategic AI Inc. v0.1.0-rc.8
Build audio plugins
From the developer
Not specified in the available declarations: capabilities, limitations, pricing & account requirements.
Build a minimal audio plugin with Dispro.
Developer declarations; not independently tested. Listing last checked Nov 7, 2026. Package observed Nov 7, 2026.
From the marketplace listing
Turn an audio-plugin idea into a scoped implementation brief, an original mono LV2 gain scaffold, and an evidence-based build handoff. Includes portable Python scripts for fixed-recipe compilation, offline audio checks, exact-byte packaging, and truthful release receipts. Native execution needs an available authorized local runtime and C compiler; ChatGPT web and mobile do not gain access to your computer. No account credentials, automatic signing, publishing, or paid services are required. This first release does not provide JUCE, AU, VST3, AAX, ASIO, or universal DAW compatibility.
Language: English · Automatically detected from descriptions.
Exact text from the indicated source. A mention alone does not establish support for your task.
Build audio-plugin scaffolds and audit release evidence with an offline, deterministic reference workflow.
· 1 observation
Package or technical metadata updated
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--- name: build-audio-plugin description: Build a small audio-plugin scaffold, turn an effect idea into a concrete implementation brief, or check build and release evidence. Use Dispro for audio-plugin development and bounded verification, not unrelated media editing. --- # Dispro This release candidate is free to use under the proprietary toolkit LICENSE. Users may modify, distribute and sell generated audio plugins under OUTPUT-LICENSE.txt, including the supplied template material. That permission does not cover modifying or redistributing the reusable toolkit or branding. Preserve upstream notices. Independently authored user work retains its existing rights. Public directory publication remains a separate gate. Support: support@kaizendsp.com. Help the user move from an audio-effect idea to a small, reviewable artifact. Start with the smallest useful change and reuse already verified results. Do not launch extra agents, repeated model calls, CI, paid services, or native build loops for routine checks. The deterministic scripts need no model API. ## Choose the current deliverable For a new effect, start with `references/guided-setup.md`: reuse known answers, ask only the missing sound/control/target choices, state defaults and show one coherent mockup. Keep the first interaction short. Load the parameter, real-time or comparison reference only when that part of the work is needed. For a new plugin or DSP design, produce a concise brief and visual mockup first: signal path, channel layout, controls/ranges, state/automation behavior, one target format/OS and observable acceptance checks. Obtain explicit approval of the mockup before implementing new DSP. Do not interpret approval of a concept or another product as approval of this mockup. If image/design tools are absent, provide a clearly labelled wireframe and keep implementation pending approval. For a headless effect, show a control/signal-flow mockup and seek the same approval. See `references/engineering-lessons.md#visual-first` for the handoff. An explicit request to exercise the existing mono LV2 gain example may use its unchanged recipe directly; this is not a new DSP design. During delivery repairs, preserve the product's approved sound/UI. Do not expand a repair into a redesign. For a first working artifact, use the original fixed gain recipe in `scripts/`. It is a real LV2 bundle with input, output and a linear 0–2 gain control. It has no custom UI, MIDI or state extension. Model-assisted design and code edits are a separate phase; v0.1's trusted runner intentionally refuses modified recipes. A new DSP design can be delivered as clearly unverified source and a reviewable validation plan, but must not be passed off as a tested recipe. ## Check execution access truthfully On ChatGPT web/mobile, provide the brief, source/commands when file tools are available, and a local handoff. An uploaded skill or ZIP does not grant access to the user's computer. Do not fabricate a local process, ask for credentials, deploy an execution bridge, or install persistent access. If the current host offers an authorized sandbox, distinguish that sandbox from the user's machine. On a local Codex/runtime host, use a new dedicated workspace chosen for this task. Do not touch another product checkout, installed plugins, DAWs, global settings, signing keys, or running processes. Use the optional Dispro MCP tools only if already configured; otherwise the bundled CLI works without installing anything. Inspect `references/workflow.md` before execution and `references/release-gates.md` before claiming release readiness. Use `references/release-checklist.md` to review an existing candidate. Consult only the applicable entries in `references/engineering-lessons.md`; these are general lessons, not permission to copy product code or a claim of new support. For controls/recall, use `references/parameter-state-contract.md`. For a changed processing path, use `references/realtime-safety.md`
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Referenced files: 18 — file references are metadata, not package contents.
Adds platform release review for asset staging, per-architecture macOS floors/preflight, separate AAX and Windows publisher signature evidence, and publication-scope checks. Original runtime validated through the installed private skill on macOS arm64; no new DSP or platform support.
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Package observed Nov 7, 2026.
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