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/cass-2003/local-workflow-skill/speechnpx skills add cass-2003/local-workflow-skill --skill speechgit clone --depth 1 https://github.com/cass-2003/local-workflow-skillWrote 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/cass-2003/local-workflow-skill/speech)<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/speech"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/speech.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 | $0.00070 | $0.01707 |
| Opus 5 | $0.00035 | $0.00853 |
| Sonnet 5 | $0.00014 | $0.00341 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
speech 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.
This is a copy
100% identical to speech — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Speech Generation Skill
Generate spoken audio for the current project (narration, product demo voiceover, IVR prompts, accessibility reads). Defaults to gpt-4o-mini-tts-2025-12-15 and built-in voices, and prefers the bundled CLI for deterministic, reproducible runs.
When to use
- Generate a single spoken clip from text
- Generate a batch of prompts (many lines, many files)
Decision tree (single vs batch)
- If the user provides multiple lines/prompts or wants many outputs -> batch
- Else -> single
Workflow
- Decide intent: single vs batch (see decision tree above).
- Collect inputs up front: exact text (verbatim), desired voice, delivery style, format, and any constraints.
- If batch: write a temporary JSONL under tmp/ (one job per line), run once, then delete the JSONL.
- Augment instructions into a short labeled spec without rewriting the input text.
- Run the bundled CLI (
scripts/text_to_speech.py) with sensible defaults (see references/cli.md). - For important clips, validate: intelligibility, pacing, pronunciation, and adherence to constraints.
- Iterate with a single targeted change (voice, speed, or instructions), then re-check.
- Save/return final outputs and note the final text + instructions + flags used.
Temp and output conventions
- Use
tmp/speech/for intermediate files (for example JSONL batches); delete when done. - Write final artifacts under
output/speech/when working in this repo. - Use
--outor--out-dirto control output paths; keep filenames stable and descriptive.
Dependencies (install if missing)
Prefer uv for dependency management.
Python packages:
uv pip install openai
If uv is unavailable:
python3 -m pip install openai
Environment
OPENAI_API_KEYmust be set for live API calls.
If the key is missing, give the user these steps:
- Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys
- Set
OPENAI_API_KEYas an environment variable in their system. - Offer to guide them through setting the environment variable for their OS/shell if needed.
- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.
What ships with it
15 files 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.
- agents/openai.yaml 299 B
- assets/speech-small.svg 742 B
- assets/speech.png 1.2 KB
- LICENSE.txt 11 KB
- references/accessibility.md 696 B
- references/audio-api.md 902 B
- references/cli.md 3.2 KB
- references/codex-network.md 1.1 KB
- references/ivr.md 708 B
- references/narration.md 671 B
- references/prompting.md 1.5 KB
- references/sample-prompts.md 991 B
- references/voice-directions.md 2.1 KB
- references/voiceover.md 790 B
- scripts/text_to_speech.py 15 KB runs code
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 · 145 lines · 70 tokens per session scan A d6120efc03ea
speech is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 1mo ago), licensed MIT. It adds 70 tokens to every session and 1,707 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to speech, differing in 0 lines, and is treated as a copy.
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