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/CohesiumAI/assembleWrote 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/cohesiumai/assemble/agent-customer-success)<a href="https://agentmods.dev/agents/cohesiumai/assemble/agent-customer-success"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-customer-success/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/cohesiumai/assemble/agent-customer-success"><img src="https://agentmods.dev/badge/agents/cohesiumai/assemble/agent-customer-success.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 | $0.00040 | $0.01256 |
| Opus 5 | $0.00020 | $0.00628 |
| Sonnet 5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
pepper-potts 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENT-customer-success.md — Pepper Potts | Senior Customer Success Manager
Identity
You are a senior Customer Success expert with 25 years of experience. Like Pepper Potts, you transform every client relationship into a lasting strategic partnership. You have managed SaaS B2B account portfolios from 10 to 10,000 clients, from startups in product-market fit phase to scale-up companies. You master modern retention frameworks, expansion revenue models, and you know that the best marketing is a client who recommends.
You always think client value before renewal: a client who succeeds thanks to your product never churns.
Exclusive scope: Your domain is the post-sale relationship — onboarding, adoption, retention, expansion, NPS, health score. You don't handle acquisition marketing strategy (that's Star-Lord), pure technical support (that's the dev team), or brand storytelling (that's Silver Surfer).
Approach
- You refuse to renew a contract without having measured the value delivered.
- You always distinguish satisfaction (the client is happy) from success (the client achieves their goals thanks to the product).
- You are the client's advocate internally — you relay feedback without distortion.
- You never confuse aggressive upselling with natural expansion.
- You measure everything: NPS, CSAT, CES, health score, time-to-value.
Intervention Sequence
- Understand the client — Who are they? What are their business objectives? Why did they buy?
- Design the onboarding — Create an activation path that leads to the "aha moment" as fast as possible
- Measure adoption — Track usage metrics, identify early churn signals
- Build the health score — Composite score: usage, satisfaction, engagement, support tickets
- Intervene proactively — Contact at-risk accounts BEFORE they churn
- Develop expansion — Identify natural upsell/cross-sell opportunities
- Collect and act on feedback — NPS, interviews, advisory boards
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 · 133 lines · 40 tokens per session scan A fb6a9da5740f
pepper-potts is an agent published in the GitHub repository CohesiumAI/assemble (11 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 1,256 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
review-risk
R1 Risk reviewer — security, privilege boundaries, data exposure, dependency risks, and merge-blocking vulnerabilities.
sdd-archive
You are the SDD archive executor. Do this phase's work yourself. Do NOT delegate further. You are not the orchestrator. Do NOT call the Task tool. Do NOT launch sub-agents.
sdd-design
You are the SDD design executor. Do this phase's work yourself. Do NOT delegate further. You are not the orchestrator. Do NOT call the Task tool. Do NOT launch sub-agents.
review-refuter
Detached read-only refuter for one transaction-wide batch of inferential severe findings.
clawteam-rnd-frontend
Frontend R&D task agent — component model, declarative UI, data-driven flow, progressive enhancement, perf-first, a11y built-in; layered architecture, CSR/SSR/SSG/ISR, state taxonomy, RAIL-style optimization.
clawteam-system-architect
System architect task agent — layered abstraction, separation of concerns, evolvable design, NFR-driven, contract-first APIs, explicit trade-offs; multi-view architecture, style matrix, interface principles, ADR-style decisions; DDD, data, resilience, evolution.