Use when a skill fails for a missing tool or the environment needs checking. Diagnoses Python, R, Node, Claude Code, Git, Zotero and configured MCP servers and prints a pass/fail table with links to the setup docs. Read-only; installs nothing.
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See how to installReview the instructions the agent will receive before you install.
Diagnose only. Never install, upgrade or configure anything — no brew install,
winget install, pip install, Rscript -e 'install.packages(...)', and no edits to
~/.claude.json or any MCP configuration (read claude mcp list only) — because auto-installers
for system Python and R are a support nightmare for non-developer users. If a tool is missing,
the user follows the setup doc themselves. Skill versions and content are out of scope
(validate_skills.sh, /manage-project status).
The diagnostic table is in English so it can be pasted into a GitHub issue. Remediation links
point to the docs/setup/ guides in the medsci-skills repository, and only these five exist:
docs/setup/README.md, docs/setup/mac.md, docs/setup/windows.md, docs/setup/mcp-setup.md,
docs/setup/common-issues.md.
uname -s
Darwin → macOS → docs/setup/mac.mdLinux → Linux (similar tooling to Mac) → docs/setup/mac.mdMINGW*, MSYS*, CYGWIN*, or detection failure on Windows → docs/setup/windows.mdRead references/setup-checklist.md and run every check in it — R1-R5 required, O1-O5 optional —
with its detect command, version command, minimum version and status rule. Use command -v <tool>
first to detect presence without running it (avoids triggering long initialization). Capture the
version output and the exit code, and report the version exactly as printed: if a command fails,
report the failure verbatim rather than a version that is "probably" installed. If command -v
finds the tool but the version command or its parse fails, mark the row ❌ and report the failing
command — the install is broken, not just outdated. An MCP server is ✅ only when
claude mcp list shows it connected; configured-but-disconnected is ⚠️.
Print a single Markdown table to stdout in this exact format, one row per check, with the doc link from the reference for the detected OS:
## MedSci Skills Setup Diagnostic
OS detected: <macOS | Linux | Windows>
Date: <YYYY-MM-DD>
| Component | Status | Detected | Required | Action |
|---|:---:|---|---|---|
| Python 3.11+ | ✅ / ❌ | 3.11.9 | 3.11+ | OK / See docs/setup/mac.md Step 2 |
| MCP: zotero | ✅ / ❌ / ⚠️ | Connected | optional | OK / See docs/setup/mcp-setup.md |
Summary: <X required components passed, Y missing>
Next step: <one-sentence action>
Status legend:
Use the reference's Summary Rules for the closing sentence.
cd ~/medsci-skills/demo/01_wisconsin_bc && claude '/orchestrate --e2e'.lit-sync, verify-refs and write-paper are smoother with Zotero MCP; link
docs/setup/mcp-setup.md.The diagnostic report goes to stdout. If the user asks for a copyable report (e.g. for a GitHub
issue), also write it to ~/.medsci-skills/diagnostic-YYYY-MM-DD.md and tell them where it is.
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Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.
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Use when a target journal has no profile yet. Reads the journal's author guidelines and writes a detailed /write-paper profile plus a compact /find-journal profile, public or user-local private, in the canonical format with quality gates.
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Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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Renamed to /model-selection in v6 (removed in v7).
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Use when analyzing a researcher's publication record from PubMed. Fetches an author's papers, classifies study types and author position, charts the patterns and writes a strategy report, with an optional trajectory-archetype classification. Works from PubMed metadata only.
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Use when one validated cohort analysis must be repeated across many exposure/outcome pairs. Generates one R/Python script per combination from a single methodology template, changing only the variables, and aggregates the results into a summary matrix.
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