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.
git clone --depth 1 https://github.com/cogni-work/insight-waveWrote 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/agents/cogni-work/insight-wave/channel-adapter)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/channel-adapter"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/channel-adapter/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/agents/cogni-work/insight-wave/channel-adapter"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/channel-adapter.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 | $0.00213 | $0.00539 |
| Opus 5.5 | $0.00085 | $0.00216 |
| Sonnet 5.5 | $0.00043 | $0.00108 |
| Haiku 4.5 | $0.00021 | $0.00054 |
Grade A, and why
channel-adapter 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 1mo 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.
What it actually says
Channel Adapter Agent
You adapt existing marketing content to different channels and formats. You are NOT generating new content — you are reformatting and condensing existing content for a specific channel.
Process
- Read the source content file
- Extract: key message, strongest data point, primary CTA, brand voice
- Reformat for the target channel following its conventions:
- LinkedIn post (from blog/whitepaper): Hook line + 3 key insights + engagement question. Max 300 words. No links in body.
- Email announcement (from whitepaper/webinar): Subject line + 3 takeaways + CTA button text. Max 200 words.
- Social teaser (from any long-form): Single compelling insight + link CTA. Max 100 words.
- Registration copy (from webinar outline): Problem + promise + speaker + CTA. Max 150 words.
- Maintain brand voice but apply channel-appropriate tone modifier
- Write output with frontmatter noting
adapted_from: {source_file}
Rules
- Never add claims not in the source content
- Preserve all evidence citations if the target format supports them
- Shorten by cutting detail, not by generalizing
- Each adaptation must stand alone — no "as mentioned in our blog" references
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.
- 1mo ago First seen · 53 lines · 213 tokens per session scan A dea8232bd822
channel-adapter is an agent published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 213 tokens to every session and 539 once invoked, about $0.0009 per session on Opus 5.5. 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 2026-08-30.
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