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 serejaris/kimi-skills --skill audience-adaptive-commsgit clone --depth 1 https://github.com/serejaris/kimi-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/serejaris/kimi-skills/audience-adaptive-comms)<a href="https://agentmods.dev/skills/serejaris/kimi-skills/audience-adaptive-comms"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/audience-adaptive-comms/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/serejaris/kimi-skills/audience-adaptive-comms"><img src="https://agentmods.dev/badge/skills/serejaris/kimi-skills/audience-adaptive-comms.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 | $0.00081 | $0.02409 |
| Opus 5 | $0.00041 | $0.01205 |
| Sonnet 5 | $0.00016 | $0.00482 |
| Haiku 4.5 | $0.00008 | $0.00241 |
Grade A, and why
audience-adaptive-comms 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 — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
audience-adaptive-comms
Automatically adjusts the granularity, language, and focus of communication content based on the target audience role. Supports four common audience types—CEO, VP, Tech Lead, and Operations—covering both upward reporting and cross-functional communication scenarios.
Workflow
Step 1: Gather Raw Information
Ask the user for the following:
- Communication topic: What they're reporting or syncing on (project progress, issue escalation, decision request, results showcase, etc.)
- Raw material: All details the user has (can be rough notes, technical docs, data reports, chat logs, or any other format)
- Target audience: Who will read this (CEO / VP / Tech Lead / Operations / Other role—if the audience doesn't fit the four types above, ask the user to describe the role's function and priorities, then adapt from the closest audience strategy)
- Communication purpose: Status update, resource request, decision request, risk alert, or results showcase
- Output format: Email, meeting talking points, Slack/Teams message, slide outline, or formal document
If the user has already specified some of this in their initial request, skip the corresponding confirmation.
Step 2: Determine Audience Profile and Adaptation Strategy
Based on the target audience, automatically apply the following adaptation strategies:
Audience Adaptation Strategies
CEO / Founder
Key concerns: Strategic impact, business value, key decision points, risks and opportunities
Granularity adjustment:
- Highest level of abstraction—keep only conclusions and decision items
- Entire briefing should fit on 1 page or within 3 minutes of verbal delivery
- Remove all technical implementation details and process descriptions
- Keep only the 1–3 most critical metrics
Language style:
- Use business language, not technical jargon
- "We completed the microservices migration" → "System reliability improved 40%, supporting 3x traffic growth next quarter"
- "Database query optimization" → "User experience improved—page load time reduced by 2 seconds"
- Avoid abbreviations and industry jargon unless the CEO is known to be familiar with them
What ships with it
1 file 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.
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 · 286 lines · 81 tokens per session scan A c60f8bfb9f24
audience-adaptive-comms is a skill published in the GitHub repository serejaris/kimi-skills (6 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 2,409 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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