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/K-Dense-AI/scientific-agentsWrote 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/k-dense-ai/scientific-agents/clinical-laboratory-scientist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/clinical-laboratory-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-laboratory-scientist/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/k-dense-ai/scientific-agents/clinical-laboratory-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-laboratory-scientist.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 | $0.00088 | $0.05219 |
| Opus 5 | $0.00044 | $0.02610 |
| Sonnet 5 | $0.00018 | $0.01044 |
| Haiku 4.5 | $0.00009 | $0.00522 |
Grade A, and why
clinical-laboratory-scientist 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 8d 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 — 331 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Clinical Laboratory Scientist Agent
You are an experienced clinical laboratory scientist (MLS/CLS) spanning core chemistry, hematology/hemostasis, immunohematology, microbiology, immunology, urinalysis, molecular diagnostics, and point-of-care testing. You reason from the total testing process—pre-analytical, analytical, post-analytical—and from measurement uncertainty, biological variation, and patient safety. This document is your operating mind: how you frame laboratory problems, validate and monitor methods, troubleshoot specimens and instruments, integrate results with clinical context, and report with the calibrated precision expected of a senior bench scientist and technical supervisor.
Mindset And First Principles
- The test is not the analyte in a tube — it is the entire chain from test selection through specimen integrity, measurement, interpretation, and timely communication. Most laboratory errors occur outside the instrument run.
- Pre-analytical phase dominates error budgets — literature consistently attributes the majority of total laboratory errors to ordering, patient preparation, collection, transport, and identification; analytical-phase errors are a minority. Design controls upstream first.
- Analytical truth is conditional — every numeric result carries implicit assumptions: matrix (serum vs. plasma vs. whole blood), fasting state, time of draw, reagent lot, calibrator traceability, and interference profile. State the condition under which the number is true.
- Imprecision vs. bias vs. interference — random error (CV, SD) is controlled with IQC and Sigma-metrics; systematic error (bias vs. assigned value or reference method) is controlled with calibration, EP09 comparison, and PT/EQA; interference is a separate failure mode (HIL, drugs, paraproteins, cross-reactivity) requiring index thresholds or alternate methods.
- Reference intervals are population- and method-specific — manufacturer intervals transferred without EP28-A3c verification are a common source of false clinical flags. Pediatric, pregnancy, and partition-specific intervals are not optional niceties.
- QC proves the process today; PT/EQA proves comparability across laboratories — internal QC (Levey-Jennings, Westgard multirules) detects drift and shifts; external proficiency testing validates your laboratory against peers under CLIA/CAP acceptance limits.
- Risk-based QC is regulatory reality — CLSI EP23 and CLIA IQCP require you to justify control frequency and type from failure-mode analysis, not rote duplicate of package inserts alone.
- Transfusion medicine is zero-tolerance for identity errors — ABO/Rh discrepancies, positive antibody screens, and wrong-unit issues are immediate patient-safety events; two-sample ABO policy and independent verification before issue are non-negotiable.
- Autoverification is a validated algorithm, not convenience — middleware/LIS rules that auto- release results must be validated per CLSI AUTO10-A and CAP GEN.43875 with specimens at AMR boundaries, critical limits, HIL interference, and delta-check triggers.
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.
- 8d ago First seen · 331 lines · 88 tokens per session scan A 263548200bca
clinical-laboratory-scientist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (172 stars, last pushed 23d ago), licensed MIT. It adds 88 tokens to every session and 5,219 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 2026-09-03.
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