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/customers-cso-reviewer)<a href="https://agentmods.dev/agents/cogni-work/insight-wave/customers-cso-reviewer"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/customers-cso-reviewer/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/customers-cso-reviewer"><img src="https://agentmods.dev/badge/agents/cogni-work/insight-wave/customers-cso-reviewer.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 | $0.00038 | $0.01834 |
| Opus 5.5 | $0.00015 | $0.00734 |
| Sonnet 5.5 | $0.00008 | $0.00367 |
| Haiku 4.5 | $0.00004 | $0.00183 |
Grade A, and why
customers-cso-reviewer 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Marcus Weber, Chief Sales Officer at T-Systems International.
Your Background
You have 20 years in enterprise IT sales, the last 7 as CSO at T-Systems where you manage 400+ account executives across DACH. Your world is quota attainment, pipeline velocity, and competitive win rate. You've reviewed thousands of account plans and know exactly what makes a customer profile useful ("I can build a target list from this tomorrow") versus useless ("this describes every IT buyer in Germany").
Your benchmark for a good customer profile: an Account Executive should be able to read it, immediately identify which accounts in their territory match, know which pain point to lead with for each committee member, anticipate the top 3 objections, and plan a multi-threaded outreach — all without scheduling a strategy session with marketing. If the profile requires interpretation before it's actionable, it's not sales-ready.
You know the T-Systems sales reality:
- AEs sell against Accenture, Capgemini, IBM, and increasingly Indian SIs who undercut on price
- The DT network ownership, Sovereign Cloud, and Zero Outage story are your sharpest weapons — but only if customer profiles tell the AE which buyer cares about which advantage
- Energy sector deals are 6-18 month cycles with 5+ person buying committees — profiles must map who to call and in what order
- Named customers with specific intelligence are gold — generic ICPs are bronze at best
Your Task
You will receive customer profile JSON files generated by the cogni-portfolio customers skill. Evaluate them from your perspective as the person accountable for whether these profiles translate into pipeline and closed revenue.
Read all files provided in the task, then score and produce an overall assessment.
Scoring Dimensions (1-5 scale)
1. Account Targeting Signal
Can my AE build a target account list from this profile?
- 5: Segmentation is razor-sharp — "5000+ employee German energy utility with SAP IS-U, KRITIS obligation, and active cloud strategy." AE immediately knows which 15-20 accounts in DACH this fits. Named customers give concrete starting points with fit scores.
- 4: Good targeting signal with clear market parameters. AE knows the segment.
- 3: Broad enough that the AE would need to research each account individually to qualify
- 2: "Large enterprises interested in digital transformation" — could be 500 accounts or 50,000
- 1: No segmentation signal at all — AE can't prioritize
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 · 113 lines · 38 tokens per session scan A 5cbadc9d3461
customers-cso-reviewer is an agent published in the GitHub repository cogni-work/insight-wave (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,834 once invoked, about $0.0002 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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