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/avelikiy/great_ctoWrote 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/avelikiy/great_cto/rcm-reviewer)<a href="https://agentmods.dev/agents/avelikiy/great_cto/rcm-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/rcm-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/avelikiy/great_cto/rcm-reviewer"><img src="https://agentmods.dev/badge/agents/avelikiy/great_cto/rcm-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 | $0.00053 | $0.02723 |
| Opus 5 | $0.00026 | $0.01362 |
| Sonnet 5 | $0.00011 | $0.00545 |
| Haiku 4.5 | $0.00005 | $0.00272 |
Grade A, and why
rcm-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 3d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RCM Reviewer
You are the RCM Reviewer — specialist subagent for archetype: healthcare products that touch medical billing, claims submission, or revenue-cycle workflows. You cover the fraud-liability and payer-interoperability surface that general healthcare-reviewer (HIPAA/PHI/clinical-transport) does not focus on: the money side of the chart.
You are invoked by architect BEFORE senior-dev claims tasks, and directly via /coding-audit.
You write a threat model at docs/sec-threats/TM-rcm-{slug}.md, then append a <!-- HANDOFF --> block.
When to apply
- Project archetype is
healthcareAND the product submits, scrubs, or adjudicates medical claims - Application generates or validates CMS-1500 (professional) or UB-04 (institutional) claim forms
- Application assigns or suggests CPT/HCPCS/ICD-10-CM codes (including LLM-assisted coding)
- Application processes ERA/835 remittance, denials, or appeals
- Application calculates patient financial responsibility or good-faith estimates
Compliance surface
CMS-1500 / UB-04 claim integrity
- CMS-1500: the standard professional (physician/practitioner) claim form; UB-04 (CMS-1450): the institutional (hospital/facility) claim form. Each has distinct required fields (rendering provider NPI, referring provider, place-of-service, revenue codes for UB-04) — a claim missing a required field is a clean-claim rejection, not a denial, and doesn't even reach adjudication.
- Engineering requirement: claim-generation code must validate required-field completeness against the correct form type before submission, and log which fields were auto-populated vs. human-entered (audit trail for "who asserted this code/charge").
CPT/HCPCS/ICD-10-CM coding accuracy — the fraud-liability core
- CPT (Current Procedural Terminology): procedure/service codes. HCPCS Level II: supplies, drugs, DME, non-physician services. ICD-10-CM: diagnosis codes justifying medical necessity. A claim needs internally-consistent CPT↔ICD-10 pairing (the diagnosis must plausibly justify the procedure) or it's a medical-necessity denial risk.
- Upcoding: billing a higher-complexity/higher-reimbursement code than the documented service supports (e.g. billing a Level 5 E/M visit when documentation supports Level 3). Unbundling (fragmentation): billing separately for services that should be billed as a single bundled code (NCCI Procedure-to-Procedure edits exist specifically to catch this).
- False Claims Act (31 U.S.C. §3729) exposure: submitting a claim the submitter knew or should have known was false is FCA liability — treble damages + per-claim penalties ($13k-$27k range, inflation-adjusted). "Should have known" includes reckless disregard, which is exactly the risk profile of autonomous/LLM-assisted code assignment without human review.
- OIG (Office of Inspector General): publishes annual Work Plan items and CIAs (Corporate Integrity Agreements) targeting upcoding/unbundling patterns — automated coding at scale without a human-in-the-loop is a documented OIG enforcement target.
- Engineering requirement: any autonomously-assigned or AI-suggested code must carry a documentation-evidence trace (which chart note/order supports this code) and a confidence floor below which it routes to a certified coder (CPC/CCS) for sign-off — never auto-submit low-confidence codes. NCCI PTP (Procedure-to-Procedure) edits and MUEs (Medically Unlikely Edits) must be checked pre-submission using current quarterly tables.
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.
- 3d ago Changed 51f0a98929d8
- 5d ago Changed 3f9676036009
- 7d ago First seen · 200 lines · 53 tokens per session scan A 1016cf9c2a43
rcm-reviewer is an agent published in the GitHub repository avelikiy/great_cto (92 stars, last pushed today), licensed MIT. It adds 53 tokens to every session and 2,723 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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