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 solo-super/marketplace --skill voice-analyzergit clone --depth 1 https://github.com/solo-super/marketplaceWrote 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/solo-super/marketplace/voice-analyzer)<a href="https://agentmods.dev/skills/solo-super/marketplace/voice-analyzer"><img src="https://agentmods.dev/badge/skills/solo-super/marketplace/voice-analyzer/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/solo-super/marketplace/voice-analyzer"><img src="https://agentmods.dev/badge/skills/solo-super/marketplace/voice-analyzer.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 | $0.00040 | $0.02413 |
| Opus 5 | $0.00020 | $0.01207 |
| Sonnet 5 | $0.00008 | $0.00483 |
| Haiku 4.5 | $0.00004 | $0.00241 |
Grade A, and why
voice-analyzer 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 — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The output becomes infrastructure—a reference document used every time you work with AI to maintain your authentic voice instead of producing generic content.
<quick_start> Provide 3-5 writing samples where your voice feels strongest (500-2000 words each). The skill will:
- Analyze patterns across all samples
- Identify your distinctive voice markers
- Generate a VOICE.md style guide
- Create a forbidden phrases list specific to your anti-patterns
- Provide testing prompts to validate the guide
Ideal samples: Newsletter issues, blog posts, emails you're proud of, social media threads that felt "like you"
Avoid: Heavily edited corporate content, collaborative pieces, anything that felt forced </quick_start>
<sample_gathering_guidance> <what_makes_good_samples> Include samples that:
- You wrote when feeling confident and natural
- Received feedback like "this sounds just like you"
- You'd be happy to write again
- Show your voice across different contexts (casual, professional, explanatory)
- Are at least 500 words (longer is better for pattern detection)
Avoid samples that:
- Were heavily edited by others
- Feel generic or corporate even to you
- Were written under heavy constraints
- Don't represent how you want to sound going forward </what_makes_good_samples>
<minimum_requirements>
- Minimum: 3 samples, 500+ words each
- Ideal: 5-7 samples, 1000+ words each
- Advanced: 10+ samples including different formats (email, long-form, social)
More samples = more accurate pattern detection, but diminishing returns after 10. </minimum_requirements>
<alternative_sources> If you don't have strong writing samples:
Voice memo transcription: Record yourself explaining something you know well. Transcribe it. Your speaking patterns often reveal writing voice better than forced writing.
Email archaeology: Search sent mail for messages where you were being genuinely helpful, not performing professionalism.
Chat/Slack messages: Longer explanatory messages often show natural voice patterns.
Old social media: Before you started "performing" for algorithms. </alternative_sources> </sample_gathering_guidance>
<analysis_framework> When analyzing samples, examine these dimensions:
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 · 311 lines · 40 tokens per session scan A 76d7a2356a65
voice-analyzer is a skill published in the GitHub repository solo-super/marketplace (2 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 2,413 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.
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