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 dailyaiagents-cpu/dailyai-os --skill voice-gategit clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-osWrote 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/dailyaiagents-cpu/dailyai-os/voice-gate)<a href="https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/voice-gate"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/voice-gate/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/dailyaiagents-cpu/dailyai-os/voice-gate"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/voice-gate.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 | $0.00075 | $0.00824 |
| Opus 5 | $0.00037 | $0.00412 |
| Sonnet 5 | $0.00015 | $0.00165 |
| Haiku 4.5 | $0.00007 | $0.00082 |
Grade A, and why
voice-gate 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 11d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
voice-gate (advisory)
What this is, what this isn't
This skill is a coach, not a gatekeeper. It runs the VOICE.md rubric over a draft and flags concerns. It does NOT block publish. The author reviews findings and decides whether each is a real issue or a false positive.
Why advisory: regex-based phrase detection can't reliably distinguish use from citation. "Leverage" the verb is fine in Cooper's writing; "leverage synergistic value" is not. Without an LLM judge, the rubric over-fires on legitimate prose. Calibrated to threshold 70 against Cooper's existing writing samples, but VISION.md (43.5) and the architecture audit (0) score below threshold because they cite the very phrases they're warning against.
What this catches reliably: clean LLM-default drafts. "We're leveraging AI to revolutionize..." scores ~70 even with the conservative weights — and a human reading the findings sees the three banned phrases highlighted, knows to rewrite.
What this misses: subtle drift, generic-but-not-cliché writing, content that's technically clean but voicelessly bland.
When to use
Before publishing any of:
- Newsletter drafts
- Friday ship logs
- Founder's monthly letters
- Sales page edits
- Reddit/LinkedIn outreach drafts
- Solo AI Founder Brief issues
- Public site copy edits
Don't use for: postmortems Cooper writes himself, internal decisions docs, code comments, Telegram replies, vault notes, audit doc sections.
Procedure
python3 ${REPO_ROOT}/tools/voice/score.py "$PATH_TO_DRAFT" "$SURFACE"
Surface options: default, newsletter, brief, product, postmortem, telegram. Each has a different sentence-length target.
Output is JSON with:
score: 0-100passes: true if ≥70 (advisory threshold)stats: sentence count, paragraph count, avg sentence wordsfindings: per-category deductions (banned-phrases / sentence-length / specificity / passive-voice / filler-tokens)
For each finding, the author considers:
- Is the flagged phrase actually a problem in this context, or a false positive?
- If it's a problem, is the suggested fix worth the rewrite cost?
- If multiple findings, prioritize banned-phrase hits first (highest signal)
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.
- 11d ago First seen · 70 lines · 75 tokens per session scan A 1fb68b4b9965
voice-gate is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 4mo ago), licensed MIT. It adds 75 tokens to every session and 824 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 2026-08-31.
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