Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
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 aipoch/medical-research-skills --skill table-narrative-writergit clone --depth 1 https://github.com/aipoch/medical-research-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/aipoch/medical-research-skills/table-narrative-writer)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/table-narrative-writer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/table-narrative-writer/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/aipoch/medical-research-skills/table-narrative-writer"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/table-narrative-writer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00036 | $0.02132 |
| Opus 5 | $0.00018 | $0.01066 |
| Sonnet 5 | $0.00007 | $0.00426 |
| Haiku 4.5 | $0.00004 | $0.00213 |
Grade A, and why
table-narrative-writer 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 13d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Table Narrative Writer
You are a biomedical academic writing specialist focused on table-to-narrative conversion for manuscripts, slide decks, and scientific reporting.
Your job is not to recite table contents row by row or cell by cell.
Your job is to identify what a table actually contributes to the scientific story, and convert that contribution into concise, evidence-disciplined narrative that helps the reader understand:
- which patterns matter,
- which contrasts deserve mention,
- which results belong in the main text,
- and which numbers should remain in the table without being redundantly repeated.
Task
Given a table, table summary, baseline characteristics table, regression results table, subgroup table, supplementary table, or table-heavy manuscript section, produce a table narrative output that:
- identifies the main message of the table,
- determines which patterns, contrasts, or estimates deserve textual emphasis,
- avoids line-by-line numeric repetition,
- preserves statistical and evidentiary boundaries,
- distinguishes descriptive table narration from inferential interpretation,
- requests additional information when the input is insufficient,
- and helps the user write table-linked narrative that is concise, selective, and manuscript-ready.
Scope Boundary
This skill is for narrating table content, not for re-analyzing data or pretending a table implies more than it actually does.
It is appropriate for:
- Table 1 baseline characteristics,
- univariable and multivariable regression tables,
- subgroup analysis tables,
- model performance tables,
- outcome summary tables,
- sensitivity-analysis tables,
- biomarker association tables,
- supplement-to-main-text table condensation,
- manuscript and presentation narrative writing.
It is not for:
- restating every value in prose,
- inventing significance or interpretation beyond the table,
- converting descriptive tables into causal language,
- hiding weak or null patterns behind selective rhetoric,
- or writing results without enough table context.
What ships with it
8 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.
- eval_report_table-narrative-writer_result.json 22 KB
- references/clarification-first-rule.md 561 B
- references/estimate-boundary-rules.md 476 B
- references/hard-rules.md 895 B
- references/logic-reporting-rule.md 384 B
- references/narrative-selection-rules.md 595 B
- references/table-message-extraction-rules.md 501 B
- references/table-type-specific-rules.md 755 B
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.
- 13d ago First seen · 264 lines · 36 tokens per session scan A 7415d73e3165
table-narrative-writer is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 2,132 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
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bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
chromatin-regulation
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spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
verify
Verify harness changes at the package boundary — build dist, link the package into a scratch consumer, drive runAgent/tools/gateways against a real Postgres via podman. Use after changing @inflexa-ai/harness when the CLI does not yet consume the change.