Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add cogni-work/insight-wave/plugin install cogni-marketingWrote 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/content-strategy)<a href="https://agentmods.dev/skills/cogni-work/insight-wave/content-strategy"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/content-strategy.svg" alt="Measured on agentmods" 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 | $0.00135 | $0.01925 |
| Opus 5 | $0.00068 | $0.00962 |
| Sonnet 5 | $0.00027 | $0.00385 |
| Haiku 4.5 | $0.00014 | $0.00193 |
Grade A, and why
content-strategy 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 3d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Strategy
Purpose
Build a comprehensive content strategy by mapping TIPS strategic themes (GTM paths) to portfolio propositions per market, then recommending content types and formats for each cell in the 3D matrix: market × GTM path × content type.
Prerequisites
- Marketing project initialized (
marketing-project.jsonexists with sources, markets, gtm_paths)
Workflow
Step 0: Load Project Context
- Read
marketing-project.json— extract markets, GTM paths, brand config, content defaults - For each market's GTM paths, load the TIPS strategic theme data:
- Read
tips-value-model.json→ find the theme by ID - Extract: theme name, value chains (T→I→P paths), solution templates, narrative angle
- Read
- For each market, load portfolio data:
- Read propositions matching that market (
propositions/*--{market}.json) - Read competitors (
competitors/*--{market}.json) - Read customer profiles (
customers/{market}.json)
- Read propositions matching that market (
- Build a lookup: which propositions connect to which GTM path (via solution template → portfolio anchor → feature → proposition)
Step 1: Narrative Angle Extraction
For each GTM path (TIPS strategic theme), extract the "WHY NOW" narrative:
- Trend hook: The most compelling trend from the theme's value chains (T layer) — this drives thought leadership
- Implication tension: The business implication that creates urgency (I layer) — this drives demand generation
- Possibility promise: The opportunity the buyer can capture (P layer) — this drives lead generation
- Solution proof: How the portfolio delivers it (S layer + propositions) — this drives sales enablement
Store as narrative_angle per GTM path:
{
"trend_hook": "AI-driven predictive maintenance reduces unplanned downtime by 47%",
"implication_tension": "Manufacturers without predictive capabilities face 3x higher maintenance costs by 2027",
"possibility_promise": "Early adopters achieve 30% asset lifetime extension through continuous condition monitoring",
"solution_proof": "Our IoT + ML platform delivers real-time anomaly detection across all asset types"
}
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.
- 3d ago First seen · 182 lines · 135 tokens per session scan A 093467f2a19b
content-strategy is a skill published in the GitHub repository cogni-work/insight-wave (12 stars, last pushed today), licensed Apache-2.0. It adds 135 tokens to every session and 1,925 once invoked, about $0.0007 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 2026-09-04.
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