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/michael-l-i/cadence-code/wrap-upnpx skills add michael-L-i/cadence-code --skill wrap-upgit clone --depth 1 https://github.com/michael-L-i/cadence-codeWrote 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/michael-l-i/cadence-code/wrap-up)<a href="https://agentmods.dev/skills/michael-l-i/cadence-code/wrap-up"><img src="https://agentmods.dev/badge/skills/michael-l-i/cadence-code/wrap-up.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 | $0.00043 | $0.00323 |
| Opus 5 | $0.00022 | $0.00161 |
| Sonnet 5 | $0.00009 | $0.00065 |
| Haiku 4.5 | $0.00004 | $0.00032 |
Grade A, and why
wrap-up 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 4d 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
Wrap Up
Use mcp__cadence-code__voice_status,
mcp__cadence-code__voice_speak, and
mcp__cadence-code__voice_stop to end the current conversation. Antigravity
may display these as the corresponding voice_status, voice_speak, and
voice_stop tools under the cadence-code MCP server.
- Call
mcp__cadence-code__voice_status. - If
readyis false, say there is no active Cadence Code conversation to wrap up. Do not callvoice_start,voice_speak,voice_listen, orvoice_stop. - If
readyis true, callmcp__cadence-code__voice_speakwith a brief, natural goodbye andlisten_after: false. - Call
mcp__cadence-code__voice_stopexactly once withwait_for_speech: true. Do not listen again. - Confirm briefly that the conversation ended and its local speech models were
released. If the goodbye fails, still call
voice_stoponce to clean up, then show the error.
Do not start a new conversation, ask for confirmation, or do additional task work from this skill.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 28 lines · 43 tokens per session scan A 7a7da32c6fbd
wrap-up is a skill published in the GitHub repository michael-L-i/cadence-code (2 stars, last pushed 4d ago), licensed MIT. It adds 43 tokens to every session and 323 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 2026-08-31.
Other skills, from other repositories
vocello-design
Use this skill to generate well-branded interfaces and assets for Vocello (a local-first, Apple-native macOS voice-generation app — formerly QwenVoice). Contains essential design guidelines, colors, type, fonts, assets, and a Mac-app UI kit for prototyping voice-studio interfaces, marketing surfaces, and slides.
add-new-model
Use this skill when the user wants to add or port a new model architecture to MLX-VLM — mapping a Hugging Face modeltype to a new file under mlxvlm/models, writing the ModelConfig, matching layer/weight names, reusing a similar existing model, adding a test class, and validating the port. Covers vision-language…
benchmarking
Use this skill when the user wants to benchmark an MLX-VLM change and present the numbers in a PR — fork-vs-main A/B comparisons, isolated-module micro-benchmarks, median-of-N timing with warmup, peak-memory reporting, correctness checks, parameter sweeps, and self-contained reproducible bench scripts to paste into a…
convert-quantize
Use this skill when the user wants to convert a Hugging Face model to MLX or quantize/dequantize one with mlxvlm.convert, including bits and group size, quant modes (affine, mxfp4, nvfp4, mxfp8), RTN vs AWQ, mixed-bit recipes, dtype casts, calibration (text or multimodal), local vs Hub paths, revisions, uploading to…
cli-inference
Use this skill when the user wants to run or debug MLX-VLM inference from the command line, including uv run mlxvlm.generate, image/audio/video inputs, local model paths, Hugging Face model IDs, deterministic repro commands, and CLI errors around processors, prompts, model loading, or missing weights.
contributing
Use this skill when the user wants to contribute to MLX-VLM — opening a PR, where model code/config/tests go, backward-compatible config args, running the test suite, code formatting and the pre-commit hooks (black, clang-format), and PR expectations (tests, review, perf evidence). Use it to set up a change so it…