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 abmgit 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/abm)<a href="https://agentmods.dev/skills/cogni-work/insight-wave/abm"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/abm/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/abm"><img src="https://agentmods.dev/badge/skills/cogni-work/insight-wave/abm.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 | $0.00105 | $0.01670 |
| Opus 5.5 | $0.00042 | $0.00668 |
| Sonnet 5.5 | $0.00021 | $0.00334 |
| Haiku 4.5 | $0.00011 | $0.00167 |
Grade A, and why
abm 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account-Based Marketing Content
Purpose
Generate hyper-personalized content for specific named accounts. ABM content spans the full funnel — from awareness through decision — but is customized to one company's situation, challenges, and decision-makers. It's the highest-effort, highest-conversion content type.
Prerequisites
- Marketing project with GTM paths configured
- Portfolio customer profiles with named accounts (
customers/{market}.json→named_customers[]) - Recommended: portfolio propositions and solutions exist for the target market
Input Parameters
| Parameter | Required | Description |
|---|---|---|
| market | Yes | Market slug |
| account | Yes | Named account slug or company name |
| gtm_path | No | GTM path theme ID (if omitted, recommend based on account fit) |
| format | No | account-plan, personalized-email, executive-briefing, custom-landing-page. If omitted, ask |
Workflow
Step 1: Load Account Context
- Read
marketing-project.json— brand, language - Load portfolio customer data for this market:
- Named account details: company name, size, industry, known contacts
- Buyer profiles: roles, seniority, pain points, buying criteria
- Load portfolio propositions and solutions for this market
- Load TIPS data: strategic themes and trend relevance
- Account research (delegate to content-writer agent with web research):
- Company website: recent news, press releases, annual reports
- LinkedIn: key decision-maker profiles and recent posts
- Industry news: recent challenges or initiatives
- Technology signals: job postings, tech stack indicators
Step 2: Account-GTM Fit Assessment
If GTM path not specified, analyze which themes are most relevant to this account:
- Match account industry/challenges to TIPS theme narratives
- Cross-reference with portfolio propositions (which solutions fit their profile)
- Present recommendation:
Recommended GTM paths for {account_name}:
1. AI-Driven Predictive Maintenance — HIGH fit (they posted 3 maintenance engineer roles last month)
2. Cloud-Native Transformation — MEDIUM fit (still on-premises based on job postings)
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 1a2d70607d54
- 1mo ago Changed b95cc2ce2ab6
- 1mo ago First seen · 134 lines · 105 tokens per session scan A e1f90b10f596
abm is a skill published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 105 tokens to every session and 1,670 once invoked, about $0.0004 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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