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 mlopscommunity/Coding-Agents-Conference-skills --skill voice-first-planninggit clone --depth 1 https://github.com/mlopscommunity/Coding-Agents-Conference-skillsWrote 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/mlopscommunity/coding-agents-conference-skills/voice-first-planning)<a href="https://agentmods.dev/skills/mlopscommunity/coding-agents-conference-skills/voice-first-planning"><img src="https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/voice-first-planning/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/mlopscommunity/coding-agents-conference-skills/voice-first-planning"><img src="https://agentmods.dev/badge/skills/mlopscommunity/coding-agents-conference-skills/voice-first-planning.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 | $0.00050 | $0.01759 |
| Opus 5 | $0.00025 | $0.00879 |
| Sonnet 5 | $0.00010 | $0.00352 |
| Haiku 4.5 | $0.00005 | $0.00176 |
Grade A, and why
voice-first-planning 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice-First Planning
Overview
Use speech-to-text tools to dictate your initial specs, feature ideas, and architectural thoughts instead of typing them. Speaking naturally produces longer, more context-rich input because people self-edit heavily when typing but ramble freely when talking. That rambling is gold — LLMs are excellent at extracting structured meaning from unstructured speech.
Core principle: Speaking freely captures intent that is hard to express in typed text. Don't self-edit — ramble and let the LLM find the structure.
Dependency: A speech-to-text tool (WhisperFlow, macOS Dictation, or similar).
When to Use
- At the start of a new feature when you have a rough idea but haven't formalized it
- During brainstorming when you want to explore multiple approaches quickly
- When writing specs, PRDs, or design documents from scratch
- When you catch yourself staring at a blank prompt, unsure how to phrase what you want
- When explaining a bug or problem you understand intuitively but struggle to articulate in text
When NOT to Use
- During active implementation (typing precise code instructions is faster)
- For short, well-defined commands ("fix the typo on line 42")
- When you are in a shared/quiet workspace without a private space to speak
- For editing or refining an already-written spec (just edit the text directly)
Common Mistakes
| Mistake | Why it's wrong |
|---|---|
| Editing yourself while speaking | The whole point is to capture raw, unfiltered intent. Self-editing while speaking defeats the purpose — you lose the same context you lose when typing. Just talk. |
| Skipping the transcription review | Speech-to-text makes errors. Quickly scan the transcript for mangled names, technical terms, or homophones before pasting it in. A 10-second scan prevents confused output. |
| Using voice during implementation | Voice shines during planning and ideation. Once you are writing code, typed instructions are more precise. Don't force voice where typing is better. |
| Pasting the transcript without a framing prompt | Claude Code needs to know what to do with the wall of text. Always prepend a short instruction like "Structure this into a feature spec" or "Extract the requirements from this transcript." |
| Speaking in short, clipped sentences | You are not typing. Speak in full, natural paragraphs. Explain the why, the context, the constraints, the edge cases. Longer is better — the LLM will compress it. |
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 · 134 lines · 50 tokens per session scan A 6b577f8b7d20
voice-first-planning is a skill published in the GitHub repository mlopscommunity/Coding-Agents-Conference-skills (37 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 50 tokens to every session and 1,759 once invoked, about $0.0003 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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