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 alexclowe/awesome-copilot-cowork-plugins --skill platform-native-voicegit clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-pluginsWrote 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/alexclowe/awesome-copilot-cowork-plugins/platform-native-voice)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/platform-native-voice"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/platform-native-voice/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/alexclowe/awesome-copilot-cowork-plugins/platform-native-voice"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/platform-native-voice.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 | $0.00027 | $0.01169 |
| Opus 5 | $0.00014 | $0.00584 |
| Sonnet 5 | $0.00005 | $0.00234 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
platform-native-voice 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 9d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in the native voice of each major social platform. When the user is writing or adapting social content, apply this knowledge automatically — never cross-post identical copy.
The core principle
Every platform has a register. Cross-posting identical copy across platforms reads as inauthentic to native users and underperforms in the algorithm. Native voice is not a tone setting — it is a vocabulary, sentence cadence, hook style, and posture toward the audience.
Platform voice profiles
LinkedIn — long-form professional thought:
- Posture: peer-to-peer expertise, not corporate broadcast
- Cadence: line breaks every 1-2 sentences; no walls of text
- Hook: first 2 lines visible before "see more" — must justify the click
- Vocabulary: industry-specific, but not jargon-stuffed
- Common formats: personal story → professional lesson, contrarian industry take, data-driven insight, thread-style breakdown
- Tells of cross-posting: emojis as bullet markers, hashtag spam, "🚀" or "💡" stacks, generic motivational openers
- 3-5 hashtags max, industry-specific
- End with a question that earns comments — engagement compounds in the first hour
X/Twitter — punchy and opinionated:
- Posture: have a take, defend it, engage replies
- Cadence: 280 chars per beat; threads for longer ideas
- Hook: contrarian claim, surprising stat, witty observation
- Vocabulary: punchy, often colloquial, occasionally profane (depending on brand)
- Tells of cross-posting: full-paragraph text walls, LinkedIn-style "I'm excited to announce," 8 hashtags
- 1-2 hashtags max, often none
- Thread structure: hook tweet → 3-7 beats → summary/CTA
- Reply game matters — quote-tweet with commentary, engage trending conversations
TikTok — hook-first conversational:
- Posture: friend telling you something, not brand broadcasting
- Cadence: caption supports the video; under 150 chars ideal
- Hook: pattern interrupt in the video; caption matches energy
- Vocabulary: native, casual, trend-aware, sometimes intentionally imperfect
- Tells of cross-posting: corporate-press-release captions, no hook, formal tone
- 3-5 hashtags: 1-2 trending + 2-3 niche
- Trending audio reference when on-brand
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.
- 9d ago First seen · 96 lines · 27 tokens per session scan A 0c1620b18af1
platform-native-voice is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (17 stars, last pushed 1mo ago), licensed MIT. It adds 27 tokens to every session and 1,169 once invoked, about $0.0001 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 2026-09-03.
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