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 JoelLewis/finance_skills --skill client-review-prepgit clone --depth 1 https://github.com/JoelLewis/finance_skillsWrote 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/joellewis/finance_skills/client-review-prep)<a href="https://agentmods.dev/skills/joellewis/finance_skills/client-review-prep"><img src="https://agentmods.dev/badge/skills/joellewis/finance_skills/client-review-prep/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/joellewis/finance_skills/client-review-prep"><img src="https://agentmods.dev/badge/skills/joellewis/finance_skills/client-review-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00124 | $0.05175 |
| Opus 5 | $0.00062 | $0.02587 |
| Sonnet 5 | $0.00025 | $0.01035 |
| Haiku 4.5 | $0.00012 | $0.00517 |
Grade A, and why
client-review-prep 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 12d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Client Review Preparation
Core Concepts
Client Context Assembly
The foundation of review preparation is assembling everything the advisor needs to know about the client before the meeting. This context package draws from multiple systems and should be gathered systematically rather than ad hoc.
Data to assemble:
- Client profile and household -- pull from CRM: names, ages, employment status, retirement dates, household members, service tier, assigned advisor, communication preferences
- Investment Policy Statement (IPS) -- the governing document for the relationship: stated objectives, risk tolerance, time horizon, return targets, asset allocation targets with tolerance bands, investment restrictions, benchmark selections, liquidity constraints
- Account inventory -- all accounts in the household: account types (taxable, IRA, Roth, trust, etc.), custodians, current market values, cash positions, cost basis summary
- Recent life events -- any changes since the last review: job change, retirement, marriage, divorce, inheritance, birth of child, health event, home purchase or sale, business sale. These come from CRM notes, advisor logs, and financial planning system updates.
- Prior meeting notes -- what was discussed at the last review, what action items were assigned, which items were completed. Unresolved items carry forward to the agenda.
- Financial plan status -- if the client has a financial plan: probability of success, progress toward goals (retirement funding, education, legacy), any goals that have drifted off track since the last review
- Compliance and administrative status -- date of last IPS update, date of last suitability questionnaire, advisory agreement renewal date, fee schedule, any pending compliance items
Assemble this data into a one-page client context summary that the advisor can review in under five minutes before the meeting.
Performance Review Preparation
Performance data is the centerpiece of most review conversations. The goal is not to re-create a full performance report (that is the domain of performance reporting and client reporting delivery), but to extract the key numbers and narratives the advisor needs for the discussion.
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.
- 12d ago First seen · 187 lines · 124 tokens per session scan A 8b0e5ee3a922
client-review-prep is a skill published in the GitHub repository JoelLewis/finance_skills (184 stars, last pushed 1mo ago), licensed MIT. It adds 124 tokens to every session and 5,175 once invoked, about $0.0006 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.
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