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 SkillMedev/health-and-longevity --skill bloodwork-explainergit clone --depth 1 https://github.com/SkillMedev/health-and-longevityWrote 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/skillmedev/health-and-longevity/bloodwork-explainer)<a href="https://agentmods.dev/skills/skillmedev/health-and-longevity/bloodwork-explainer"><img src="https://agentmods.dev/badge/skills/skillmedev/health-and-longevity/bloodwork-explainer/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/skillmedev/health-and-longevity/bloodwork-explainer"><img src="https://agentmods.dev/badge/skills/skillmedev/health-and-longevity/bloodwork-explainer.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 | $0.00140 | $0.01639 |
| Opus 5 | $0.00070 | $0.00820 |
| Sonnet 5 | $0.00028 | $0.00328 |
| Haiku 4.5 | $0.00014 | $0.00164 |
Grade A, and why
Bloodwork Explainer 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 10d 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.
This is a copy
100% identical to Bloodwork Explainer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bloodwork Explainer
Most people do one of two harmful things with lab results: self-diagnose from a single out-of-range value, or file the report away without understanding what it flagged. This skill does neither. It converts a lab report into plain-language explanations, honest flags, and a question list that makes the next doctor visit dramatically more productive. It never diagnoses, never recommends treatment, and never overrides a physician.
Operating procedure
Step 1: Gather inputs
Collect before interpreting anything. Missing context is the main source of bad interpretation.
- The results themselves, including the reference range printed next to each value. Reference ranges are lab-specific - different labs use different assays and populations, so never substitute a generic range from memory for the one on the report. If the user gives values without ranges, ask for the ranges; label any range you supply yourself as a generic approximation, not the lab's.
- Age and sex (many ranges differ by both).
- Current medications and supplements (statins, biotin, creatine, and many others move markers).
- Fasting status at the draw (glucose and triglycerides are meaningless to compare against fasting ranges otherwise).
- Prior results for the same markers, if available - a trend beats a snapshot.
- Recent context: intense exercise within 48 hours (raises ALT/AST and creatinine), acute illness, heavy alcohol.
- Any symptoms the user is worried about.
Step 2: Screen for urgent flags first
Before explaining anything, scan for values the lab marked as critical, and for symptom red flags. If any are present, lead with the escalation guidance below - do not bury it under education.
Step 3: Organize by panel and explain in plain language
Work panel by panel. For each marker: what it measures, whether the value sits inside the lab's range, and the common non-alarming reasons it moves.
- CBC: hemoglobin (oxygen-carrying capacity; low suggests anemia), hematocrit (percent of blood that is red cells), WBC count (immune activity; high can indicate infection or inflammation), platelets (clotting). A single out-of-range value in isolation rarely means much - trend and clinical context matter.
- CMP: ALT and AST (liver stress, heavy alcohol, or recent intense exercise), ALP (bile ducts and bone), creatinine and BUN (waste clearance), eGFR (estimated kidney filtration), electrolytes (sodium, potassium, chloride, bicarbonate), calcium. Fasting glucose: 70-99 mg/dL is the typical normal band; 100-125 is the pre-diabetic range; note that the lab's own range governs.
- Lipid panel: total cholesterol alone is a poor predictor. LDL is the primary target (under 100 mg/dL is the common optimal reference for most adults, lower for high-risk individuals - the target is a physician decision). HDL under 40 mg/dL is a risk factor. Triglycerides under 150 mg/dL is the normal reference. Suggest asking the doctor about ApoB when cardiovascular risk is the concern.
- Thyroid and hormones: TSH is the standard screen; abnormal values prompt free T4 and T3 follow-up. Testosterone (total and free) is relevant to fatigue, libido, and body-composition symptoms in both sexes. HbA1c reflects roughly 3 months of average glucose. Vitamin D (25-OH): the optimal range is debated; 30-60 ng/mL is a common clinical target.
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.
- 10d ago First seen · 70 lines · 140 tokens per session scan A 58c7051c9eac
Bloodwork Explainer is a skill published in the GitHub repository SkillMedev/health-and-longevity (1 stars, last pushed 2mo ago), licensed MIT. It adds 140 tokens to every session and 1,639 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Bloodwork Explainer, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
apple-health
Unofficial Apple Health export MCP for AI agents. Prefer MCP tools if connected; otherwise the package CLI. Use when the user wants Apple Health data or actions through an agent.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly…
pkpd-modeling
Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…
statistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required…
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
diffdock
DiffDock and DiffDock-L molecular docking. Use for protein-small-molecule pose prediction from PDB or sequence plus SMILES/SDF/MOL2, batch docking, virtual screening, and pose-confidence interpretation. Not for binding affinity prediction.