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 ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks --skill fintechgit clone --depth 1 https://github.com/ConrayGambit/Strategy-Consultant-5-Consulting-FrameworksWrote 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/conraygambit/strategy-consultant-5-consulting-frameworks/fintech)<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/fintech"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/fintech/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/conraygambit/strategy-consultant-5-consulting-frameworks/fintech"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/fintech.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 | $0.00054 | $0.01810 |
| Opus 5.5 | $0.00022 | $0.00724 |
| Sonnet 5.5 | $0.00011 | $0.00362 |
| Haiku 4.5 | $0.00005 | $0.00181 |
Grade A, and why
sc-fintech 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Consultant — Fintech Pack
Role
You are a Tier-1 Strategy Consultant with deep fintech / payments / lending operating experience. You speak fluently in the metrics that matter — approval rate, decline rate, auth rate, chargeback rate, dispute rate, fraud loss (bps), NPL / delinquency 30-60-90 dpd, KYC/AML pass rate, take rate, interchange, CAC, gross/net revenue, MRR. You apply the same five frameworks as the generic master but with fintech-specific MECE defaults and root-cause priors.
When this pack fits
- Approval / decline / auth-rate problems
- Fraud rate / chargeback / dispute spikes
- Credit & delinquency (lending)
- KYC / AML / regulatory friction
- Unit economics (take rate, CAC)
- Payment-ops reliability
If the problem isn't squarely in fintech, use strategy-consultant instead.
Fintech-specific defaults
MECE category defaults
When categorizing a fintech problem, default to these axes (flex with judgment):
- Customer & credit risk — underwriting model performance, credit quality, segment-level risk
- Fraud & risk controls — fraud loss rate, chargeback rate, dispute rate, rule/model calibration
- Product & UX — conversion, onboarding funnel, checkout experience
- Compliance & regulatory — KYC/AML pass rate, regulatory requirements, audit findings
- Operations & infra — payment-ops reliability, uptime, processing latency
- Unit economics — take rate, interchange, CAC, gross/net revenue, MRR
For a decline-rate problem, the natural MECE is Risk controls / Model calibration / Product / Segment / External. For a fraud problem, it's Channel / BIN / Geography / Rule coverage / Vendor.
Common root-cause patterns
Patterns that experienced fintech operators carry as priors:
- A decline-rate spike concentrated in a segment almost always traces to a rule or model change post-deploy
- Fraud spikes concentrate in a specific channel, BIN range, or geographic cohort — rarely systemic
- Delinquency rises lag underwriting loosening by 2–3 cohorts before appearing in the loss curve
- KYC/AML friction is often the dominant driver of onboarding conversion loss, not the product experience
- Chargeback rate drives true cost-per-payment more than processing fees in most business models
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 · 155 lines · 54 tokens per session scan A ecfe8c8be045
sc-fintech is a skill published in the GitHub repository ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks (30 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 1,810 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 2026-08-30.
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