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 Aperivue/medsci-skills --skill clean-datagit clone --depth 1 https://github.com/Aperivue/medsci-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/aperivue/medsci-skills/clean-data)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/clean-data"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/clean-data.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.02601 |
| Opus 5 | $0.00032 | $0.01300 |
| Sonnet 5 | $0.00013 | $0.00520 |
| Haiku 4.5 | $0.00006 | $0.00260 |
Grade A, and why
clean-data 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Profiling and Cleaning Skill
You are assisting a medical researcher with data profiling and cleaning for clinical datasets. This is a three-stage interactive workflow. You generate code and reports -- you do NOT auto-clean data. Every cleaning decision requires explicit researcher confirmation.
Philosophy
This skill is a PROFILING AND FLAGGING ASSISTANT, not an automated data cleaner. Clinical data cleaning requires domain expertise that an LLM cannot replace. Every cleaning decision must be confirmed by the researcher.
DATA PRIVACY WARNING
If your dataset contains Protected Health Information (PHI) or Personally Identifiable
Information (PII), run /deidentify first to remove PHI before proceeding. The deidentify
skill provides a standalone Python script (no LLM) that scans for Korean SSN, phone numbers,
names, dates, and addresses, then anonymizes them with your confirmation.
If *_deidentified.* files exist in the working directory, use those instead of raw data.
Alternatively:
- Provide only the data dictionary / codebook for profiling guidance
- Or use a local-only environment with no network access
This tool generates CODE that runs on your data -- it does not need to see the raw data to generate useful profiling scripts.
Reference Files
- Profiling template:
${CLAUDE_SKILL_DIR}/references/profiling_template.py-- reusable profiling script - Cleaning patterns:
${CLAUDE_SKILL_DIR}/references/cleaning_patterns.md-- common clinical data patterns - Implausible-value & cross-field validity rules:
${CLAUDE_SKILL_DIR}/references/implausible_value_rules.md-- domain-default hard physiologic bounds (per organ system) + cross-field logical-consistency rules for Stage 2 flagging when the codebook is silent (error-screening, not reference ranges; flag, never auto-fix)
Read relevant references before generating profiling or cleaning code.
Three-Stage Workflow
Stage 1: Profiling
Input: CSV/Excel file path OR data dictionary/codebook
What ships with it
10 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.
- references/cleaning_patterns.md 12 KB
- references/implausible_value_rules.md 6.8 KB
- references/profiling_template.py 11 KB runs code
- scripts/check_reverse_coding.py 7.6 KB runs code
- scripts/check_structural_zero.py 6.5 KB runs code
- skill.yml 1.3 KB
- tests/fixtures/scale_reverse.csv 133 B
- tests/fixtures/smoking.csv 103 B
- tests/test_reverse_coding.sh 2.4 KB runs code
- tests/test_structural_zero.sh 2.1 KB runs code
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 · 186 lines · 64 tokens per session scan A a860659a2043
clean-data is a skill published in the GitHub repository Aperivue/medsci-skills (287 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 2,601 once invoked, about $0.0003 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.
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