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 glebis/claude-skills --skill doctorggit clone --depth 1 https://github.com/glebis/claude-skillsWrote 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/glebis/claude-skills/doctorg)<a href="https://agentmods.dev/skills/glebis/claude-skills/doctorg"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/doctorg/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/glebis/claude-skills/doctorg"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/doctorg.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high System Prompt Leakage · line 160 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00045 | $0.01587 |
| Opus 5 | $0.00023 | $0.00794 |
| Sonnet 5 | $0.00009 | $0.00317 |
| Haiku 4.5 | $0.00005 | $0.00159 |
Grade A, and why
doctorg 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.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doctor G -- Evidence-Based Health Research
Answer health and wellness questions using only trusted, evidence-based sources with explicit evidence strength ratings.
Usage
# Quick answer (WebSearch only, ~30s)
/doctorg Is creatine safe for daily use?
# Deep research (WebSearch + Tavily, ~90s)
/doctorg --deep Huberman vs Attia on fasted training
# Full investigation (WebSearch + Tavily + Firecrawl, ~3min)
/doctorg --full What does current evidence say about GLP-1 agonists for non-diabetic weight loss?
# Without personal health context
/doctorg --no-personal Best stretching protocol for lower back pain
Depth Levels
| Level | Flag | Tools | Time | Use When |
|---|---|---|---|---|
| Quick | (default) | WebSearch | ~30s | Simple factual questions |
| Deep | --deep |
WebSearch + Tavily | ~90s | Competing claims, nuanced topics |
| Full | --full |
WebSearch + Tavily + Firecrawl | ~3min | Controversial topics, need primary sources |
How It Works
1. Parse Query & Detect Topic Category
Classify the question into one of:
- Nutrition/Supplements (examine.com gets priority)
- Exercise/Training (PubMed + ACSM get priority)
- Sleep (focus sleep-specific databases)
- Disease/Condition (condition-specific orgs + clinical guidelines)
- Medication/Treatment (FDA, EMA, Cochrane get priority)
- Mental Health (APA, mental health orgs)
- General Wellness (broad search across all tiers)
2. Search Evidence Sources (Tiered)
Search sources in priority order. See references/sources.md for complete domain list.
Tier 1 -- Primary Research (highest weight):
- PubMed/PMC, Cochrane Library, WHO, ClinicalTrials.gov
Tier 2 -- Clinical/Institutional (high weight):
- Mayo Clinic, Hopkins Medicine, Cleveland Clinic, Harvard Health
- Condition-specific: AHA, ACS, ADA, Alzheimer's Association
Tier 3 -- Expert Analysis (medium weight):
- Examine.com, STAT News, Health News Review
- Consensus.app, Epistemonikos
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 199 lines · 45 tokens per session scan A d9b720320cb4
doctorg is a skill published in the GitHub repository glebis/claude-skills (374 stars, last pushed 7d ago), licensed MIT. It adds 45 tokens to every session and 1,587 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…