Speech Swift is a collection of Apple Silicon speech tools for recognizing, generating, transforming, and analyzing audio locally with MLX Swift and CoreML. It is for Mac and iOS applications that need on-device speech features such as transcription, voice agents, synthesis, and speaker identification; the catalogue entries provide instructions and skills for using it.
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 skills add soniqo/speech-swift --skill review-prgit clone --depth 1 https://github.com/soniqo/speech-swiftWrote 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/soniqo/speech-swift/review-pr)<a href="https://agentmods.dev/skills/soniqo/speech-swift/review-pr"><img src="https://agentmods.dev/badge/skills/soniqo/speech-swift/review-pr/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/soniqo/speech-swift/review-pr"><img src="https://agentmods.dev/badge/skills/soniqo/speech-swift/review-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00071 | $0.00708 |
| Opus 5 | $0.00036 | $0.00354 |
| Sonnet 5 | $0.00014 | $0.00142 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
review-pr 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 10d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review PR
Review the PR from the user's point of view: make the decision easy, then provide detail only when it changes the decision.
Workflow
- Inspect PR metadata, files, diff, and checks.
- Read the touched code and nearby architecture boundaries.
- Identify whether the change is a behavior change, performance change, refactor, test-only change, docs-only change, or CI/infrastructure change.
- Run the required review passes:
- architecture fit: does the change match existing module boundaries and project patterns;
- adversarial review: what breaks under edge cases, bad inputs, scale, concurrency, cancellation, cache misses, offline mode, or hosted runner differences;
- security review: secrets, tokens, path traversal, unsafe downloads, sandbox escapes, user-controlled file paths, network exposure, dependency risk, and data leakage;
- docs review: local docs, public site docs, README translations, CLI help, model tables, benchmark pages, and migration notes when behavior is user-visible.
- Assess regression risk from the changed behavior and blast radius.
- Decide whether existing tests are enough. Include E2E coverage for runtime/model/user-facing behavior, or explicitly state why E2E is not applicable. Add or request tests only when they protect a real risk.
- Run the smallest meaningful validation:
- unit tests for logic and data structures;
- focused E2E for model/runtime behavior;
- CLI probe or benchmark only when the PR changes user-facing runtime behavior or performance.
- End with a short merge recommendation.
Output Shape
Start with this format unless the user asks for a detailed code review:
Status: ready / not ready / needs follow-up
What changed:
...
Architecture fit:
...
Adversarial/security/docs:
Adversarial: ...
Security: ...
Docs: ...
Regression risk:
Low / medium / high, with one-sentence reason.
Tests:
Unit: ...
E2E: ...
Needed: ...
Recommendation:
Merge / request changes / investigate first.
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.
- 10d ago First seen · 74 lines · 71 tokens per session scan A 5c48211cd4ed
review-pr is a skill published in the GitHub repository soniqo/speech-swift (1,171 stars, last pushed yesterday), licensed Apache-2.0. It adds 71 tokens to every session and 708 once invoked, about $0.0004 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 2026-08-30.
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apple-hig-check
Audit code or design against Apple Human Interface Guidelines.
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