Pharma Skills
Skills for pharma R&D: clinical trial design, ADaM datasets with admiral, Kaplan–Meier digitizing and statistical review.
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Why this level
- Skills bundle scripts that run in your environment and work with clinical study data.
- Outputs may be wrong and are not fit for regulatory decisions without verification.
Install
- Works in
In your terminal, with SkillFoxx CLI
npx skillfoxx add skills/pharma-skillsDetects the agents on your machine, shows the access level and pins the version.
Other ways to install
Access and checks
How we reviewExtended accessThe tool runs code or makes network calls and only reads external data.
- Skills bundle scripts that run in your environment and work with clinical study data.
- Outputs may be wrong and are not fit for regulatory decisions without verification.
This is third-party code. Review the repository files before installing.
What it does
The R Consortium repository collects agent skills for pharmaceutical research. `group-sequential-design` builds group sequential survival trials with interim analyses and spending functions. The `admiral-*` series derives ADaM datasets (ADSL, ADAE, ADRS, ADTTE, ADPC, BDS) from SDTM with the admiral R package. Others include `clinical-trial-simulation` built on the TrialSimulator package, `clinical-trial-ipd-sim` for synthetic data, `km-digitizer` for Kaplan–Meier curves, `statistical-reviewer` for independent review of a submission package and `rounding` for rounding checks. Each skill goes through community benchmarking described in `LIFECYCLE.md`.
Who it is for. Biostatisticians and clinical programmers who write R code for trials and submissions and want ready agent workflows to lean on.
Good fit when
- You compute the event count for an overall-survival trial with interim analyses.
- You derive ADSL and ADAE from SDTM domains under CDISC conventions.
- You reconstruct patient-level data from a published Kaplan–Meier curve.
- A submission package gets an independent statistical review.
Not a fit when
- Output will feed regulatory work without your own verification.
- The project has nothing to do with R and pharma.
- Organization rules forbid giving the data to an agent.
Example request
Design a group sequential overall-survival trial with two interim analyses and show the stopping boundaries.Limitations
The README warns directly: outputs are a drafting aid, not a validated deliverable. Model answers can differ between runs, so verify everything with your own QC before clinical, regulatory or GxP decisions. Skills bundle scripts that run in your environment, so read them before installing. The project carries no warranty and is not endorsed by the R Consortium. The license is stated as MIT.
How to disable. Delete the skill folders from the agent's skills directory. Exact paths depend on the agent you installed into.
FAQ
How do I install the skills?
Run `npx skills add RConsortium/pharma-skills --all` for everything or `--skill <name>` for chosen ones. The `--list` flag shows available skills without installing.
Which agents are supported?
The README names Claude Code, Codex, Cursor, Cline and Copilot through `npx skills add`. For Claude Code globally use `--agent claude-code --global`.
Can the output go into a submission?
Not without your own verification. The README requires checking outputs against organization QC before regulatory or clinical decisions.
Which skills are included?
`group-sequential-design`, `clinical-trial-simulation`, six `admiral-*` skills, `clinical-trial-ipd-sim`, `statistical-reviewer`, `rounding` and `km-digitizer`. `npx skills add RConsortium/pharma-skills --list` shows the current set.
Is R required?
Yes, the skills produce R code and use pharmaverse packages such as admiral. `km-digitizer` also generates Word reports.
Discussion
No questions or reviews yet.
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README in short
The catalog was created in Pilot 7 of the R Consortium Submissions Working Group. Install through `npx skills add` or by asking the agent in chat.
README badge
Original SKILL.md
--- name: group-sequential-design description: > Design group sequential clinical trials for survival endpoints (OS, PFS, DFS) with interim analyses, spending functions, boundaries, multiplicity, and event/enrollment prediction. Triggers on: Phase 3 trial design, sample size/events for survival endpoints, alpha spending, group sequential design, interim analysis planning, or enrollment/event timeline prediction for clinical trials. --- ## Bundled Resources | File | What it contains | When to read | |------|-----------------|--------------| | `reference.md` | Design guidance, parameter tables, spending functions, key rules, failure modes, analysis framework, protocol references | After collecting user inputs, before writing R code | | `examples.md` | All R code examples organized by design pattern | When you need code for a specific pattern — read only the relevant section | | `post_design.md` | IA timing checks (warning messages, user options), verification procedure (pass criteria, lrsim code, log template) | After computing the design (step 6), before delivering | | `scripts/gsd_report_template.py` | Bundled Python report template — reads gsd_results.json, generates gsd_report.docx with zero hardcoded values | Step 9: copy to output dir and run (or extend for design-specific features) | Do NOT read these files upfront. Read them only when you reach the corresponding step. --- ## Compact Instructions When the conversation gets long and context is compressed, preserve information in this priority order: 1. **Architecture decisions** — never re-summarize these; they are settled 2. **Key changes** — what was computed, what parameters were chosen 3. **Current verification status** — pass/fail only, drop verbose logs 4. **Tool outputs** — can be deleted; keep only essential numbers (events, boundaries, power, sample size) --- ## Workflow **Task Progress Tracking:** At the start of step 6 (after user confirms inputs), create a task checklist using `TaskCreate` for all remaining steps. Mark each task `in_progress` when starting and `completed` when done. Typical tasks: 1. Write and run R design script (gsd_design.R) 2. Check IA timing constraints 3. Run verification simulation (gsd_verification.R) 4. Generate Word report (gsd_report.py) This gives the user a visual progress indicator (spinner → checkmark) throughout the computation. **Pacing — one artifact per turn.** Do not bundle multiple major artifacts into a single response. Each of these is its own turn: - Writing `gsd_design.R` (then stop — let the tool result come back) - Running `gsd_design.R` (then stop) - Writing `gsd_verification.R`