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 cogni-work/insight-wave --skill demand-generationgit 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/skills/cogni-work/insight-wave/demand-generation)<a href="https://agentmods.dev/skills/cogni-work/insight-wave/demand-generation"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/demand-generation/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/cogni-work/insight-wave/demand-generation"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/demand-generation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00117 | $0.01579 |
| Opus 5.5 | $0.00047 | $0.00632 |
| Sonnet 5.5 | $0.00023 | $0.00316 |
| Haiku 4.5 | $0.00012 | $0.00158 |
Grade A, and why
demand-generation 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Generation Content
Purpose
Generate high-frequency, channel-optimized content that drives awareness and interest. Demand gen content bridges thought leadership (insight) and lead generation (conversion). It takes the trend hooks and proposition value messages and adapts them for social, search, and visual channels.
Prerequisites
- Marketing project with GTM paths configured
- Recommended: anchor thought leadership content exists (to derive from)
Input Parameters
| Parameter | Required | Description |
|---|---|---|
| market | Yes | Market slug |
| gtm_path | Yes | GTM path theme ID |
| format | No | linkedin-post, seo-article, carousel, video-script, infographic-spec. If omitted, ask |
Workflow
Step 1: Load Context
- Read
marketing-project.json— brand voice, content defaults, language - Read
content-strategy.json— narrative angle, planned formats - Load TIPS data: trend hook + implication tension (awareness/consideration angles)
- Load portfolio: propositions for this market (DOES/MEANS statements — the value message)
- Check for existing thought leadership content in this cell — derive from it if available
- Optional enrichment: If
sources.enriched_portfolio_narrativesexists inmarketing-project.json, read the market-level customer narrative for this market. Use it as a messaging reference for value language and buyer framing — it reflects how the portfolio speaks to this market's buyers. Do not copy verbatim; adapt for channel-specific tone.
Step 2: Generate Content
Delegate to content-writer agent with format-specific instructions:
LinkedIn Post (200-300 words)
Structure:
- Hook line (first line visible in feed — must stop the scroll): Question, bold statement, or surprising stat
- Body (8-12 lines): Short paragraphs (1-2 sentences each). Use line breaks for readability. Mix insight with practical value.
- Engagement driver: Question or call for opinions
- Hashtags: 3-5 relevant industry hashtags
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 Changed ea4a17bf42f0
- 1mo ago First seen · 138 lines · 117 tokens per session scan A 53ba98092a7d
demand-generation is a skill published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 117 tokens to every session and 1,579 once invoked, about $0.0005 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 2026-09-04.
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