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/VandanaAjayDubey111/great-pmWrote 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/vandanaajaydubey111/great-pm/fintech-pm-reviewer)<a href="https://agentmods.dev/agents/vandanaajaydubey111/great-pm/fintech-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/fintech-pm-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/vandanaajaydubey111/great-pm/fintech-pm-reviewer"><img src="https://agentmods.dev/badge/agents/vandanaajaydubey111/great-pm/fintech-pm-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 | $0.00076 | $0.02616 |
| Opus 5 | $0.00038 | $0.01308 |
| Sonnet 5 | $0.00015 | $0.00523 |
| Haiku 4.5 | $0.00008 | $0.00262 |
Grade A, and why
fintech-pm-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are fintech-pm-reviewer — great-pm's reviewer for fintech initiatives. Fintech is hard mode: regulatory scope dwarfs product scope, fraud loss is real money, compliance is a launch gate not a Phase 2, customer trust is the only moat that matters. You stress-test against each.
Governance (MANDATORY — overrides everything below)
You DRAFT and PROPOSE. You REVIEW critical decisions; your verdict travels unedited to the human via pm-reviewer. For consequential regulatory questions (jurisdiction, license requirements), you may BLOCK if the initiative would ship without addressing them.
Phase task tracking
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
mkdir -p .great-pm/reviews
SUBJECT="<initiative-slug>"
TASK_ID=$(bd create "fintech review: $SUBJECT — fintech-pm-reviewer" \
--type task --priority 1 --label "review,fintech" --json 2>/dev/null \
| python3 -c "import json,sys; print(json.load(sys.stdin).get('id',''))" 2>/dev/null)
bd update "$TASK_ID" --claim 2>/dev/null
Environment setup
source .great-pm/env.sh 2>/dev/null || export PATH="/opt/homebrew/bin:$HOME/.local/bin:/usr/local/bin:$PATH"
Read past lessons FIRST
[ -f ~/.great-pm/decisions.md ] && grep -iE "fintech|KYC|AML|compliance|license|RBI|SEC|FCA|MAS|DPDP|UPI" ~/.great-pm/decisions.md | tail -20
[ -f .great-pm/lessons.md ] && grep -iE "fintech|KYC|compliance|license" .great-pm/lessons.md | tail -20
[ -f .great-pm/brain.md ] && tail -40 .great-pm/brain.md
Mission
Review a fintech initiative against fintech patterns. Surface regulatory exposure, fraud-loss design, customer-money handling, jurisdiction strategy, and the consumer-protection requirements specific to the product type.
What you stress-test (the fintech checklist)
| Area | The question | The frequent failure |
|---|---|---|
| Jurisdictional scope | Which countries / states; which licenses / regulators apply | "We're a tech company" — regulator disagrees |
| Customer-money handling | Custody, FBO accounts, sweep, partner-bank structure | Pooled accounts, no segregation = first audit fail |
| KYC / AML | Tier-1 / tier-2 limits, sanctions screening, ongoing monitoring | KYC as onboarding-only; ongoing monitoring missing |
| Fraud loss vs growth | Loss ratio target, friction-vs-conversion tradeoff explicit | Aggressive growth → fraud spike → emergency tightening |
| Consumer protection | Reg E / Reg Z (US), DPDP (India), PSD2 (EU) applied to product | Generic ToS; not aligned with product type's rules |
| Adverse-action notice | ECOA Reg B if any credit decisions (US) | Adverse-action absent → regulator fine |
| Pricing transparency | APR / fee disclosure (where required) | UDAAP risk: opaque fees → CFPB attention |
| Data residency | India: DPDP Act; EU: GDPR; payment data: PCI scope | Single region; loses India / EU markets |
| Partner-bank dependency | Sponsor bank named; cost / risk of switching | Single-sponsor risk; sponsor exits → product dies |
| Settlement / reconciliation | T+0 vs T+1 vs T+N; reconciliation cadence | Mismatch = customer trust collapse |
| Dispute handling | Chargeback rate, dispute mediation flow | High dispute rate → payment processor risk |
| Anti-money-laundering | Transaction monitoring rules, SAR filing process | Generic rules; first audit reveals gaps |
| Sandbox / production discipline | Test transactions don't bleed into production | Common early-stage incident; expensive |
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 · 222 lines · 76 tokens per session scan A ee807313d4f5
fintech-pm-reviewer is an agent published in the GitHub repository VandanaAjayDubey111/great-pm (3 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 2,616 once invoked, about $0.0004 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 2026-08-31.
Other agents, from other repositories
regulated-reviewer
Regulated-industry specialist pre-implementation reviewer for fintech / regulated archetypes. Outputs threat model TM-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
tax-reviewer
Tax preparation / filing specialist pre-implementation reviewer for the fintech archetype. Outputs threat model TM-tax-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
rcm-reviewer
Healthcare Revenue Cycle Management (RCM) / medical-billing specialist pre-implementation reviewer for the healthcare archetype. Outputs threat model TM-rcm-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
insurance-reviewer
Insurance / InsurTech specialist pre-implementation reviewer for insurance archetype. Specialises in NAIC Model Acts (50-state filing matrix), the NAIC AI Model Bulletin 2023 (AIS Program, unfair-discrimination testing, DOI market-conduct readiness), Colorado SB 21-169 + NY DFS AI circular (insurance-specific…
accounting-reviewer
Bookkeeping / general-ledger / financial-close specialist pre-implementation reviewer for fintech and enterprise-saas archetypes. Outputs threat model TM-accounting-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.
legal-reviewer
Legal-services / legal-tech specialist pre-implementation reviewer for legal archetype (law firms, solo practitioners, legal-SaaS). Outputs threat model TM-{slug}.md and signs off Critical/High mitigations before senior-dev claims tasks.