Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bestagentkits/agency-skillsnpx agentmods add skills/bestagentkits/agency-skills/cco-reviewWrote 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/bestagentkits/agency-skills/cco-review)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/cco-review"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/cco-review/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/bestagentkits/agency-skills/cco-review"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/cco-review.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 | $0.00065 | $0.01343 |
| Opus 5 | $0.00032 | $0.00672 |
| Sonnet 5 | $0.00013 | $0.00269 |
| Haiku 4.5 | $0.00006 | $0.00134 |
Grade A, and why
cco-review 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 9d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/cs:cco-review — CCO Forcing Questions
Command: /cs:cco-review <plan>
The retention-obsessed CCO pressure-tests any plan that touches customer experience. Six questions before any retention claim, segmentation change, CS team expansion, or major CS hire.
When to Run
- Before any board narrative that includes a retention number
- Before approving a CS team headcount expansion
- Before re-segmenting the customer base or changing tier definitions
- Before launching a customer marketing or advocacy program
- Before a major CS hire (CSM, AM, Implementation, Customer Marketing)
- When NRR is "great" but churn complaints from CSMs are increasing
- Before deciding whether to add an AM role separate from CSM
The Six CCO Questions
1. What's the GROSS retention rate?
Not NRR. Gross. NRR can hide a leaky bucket behind expansion.
- GRR healthy ≥ 90% at growth stage, ≥ 95% at scale
- If GRR < 85% but NRR > 100%, the product is failing for 15%+ of customers; expansion is masking the failure
- Run
retention_decomposition_analyzer.py
2. What's the #1 reason customers leave?
If you can't name it, you don't understand churn.
- 7-category taxonomy: product_fit / competitor_loss / no_value_realized / pricing / champion_left / company_event / tactical_failure
- Preventable churn = product_fit + no_value_realized + tactical_failure
- If preventable > 50%, CS has clear leverage; if < 30%, churn is structural (ICP, market, competition)
3. What's the median time-to-value (TTV) by segment?
Long TTV signals different problems by segment.
- Long TTV in low tier = ICP misfit; downgrade or kill
- Long TTV in high tier = onboarding broken; fix the Implementation Manager handoff
- TTV is a leading indicator of GRR
4. Which customer would you fire today?
If "none" — your segmentation is broken.
- Some accounts cost more than they earn (support cost > 50% of ARR + low ICP fit)
- Run
customer_segmentation_designer.pyto surface kill list - The 3 paths for kill candidates: non-renewal / downgrade-to-tech-touch / raise-price-to-cost-recover
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 131 lines · 65 tokens per session scan A 643c159db93d
cco-review is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 1,343 once invoked, about $0.0003 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 2026-09-03.
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