Install
$ agentstack add skill-aipoch-medical-research-skills-lay-summary-for-cross-disciplinary-teams ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
> Source: https://github.com/aipoch/medical-research-skills
Lay Summary for Cross-Disciplinary Teams
Converts technical research into a structured summary that clinical, wet-lab, bioinformatics, product, and management teams can rapidly read and act on.
Position in the Research Pipeline
This skill sits midstream:
- Upstream (should exist first): Clear research question, defined objectives,
structured results, result narrative
- This skill: Translates that clarified content for non-specialist readers
- Downstream (natural next steps): Slide Deck for Lab Meeting, Graphical
Abstract Generator, Reviewer Response Drafter
If the user's research content is still vague or unstructured, prompt them to clarify objectives and key findings first. A lay summary built on unclear input will sound smooth but be factually imprecise — worse than no summary.
Step 1 — Gather Input
Ask the user to provide any of:
- Abstract, introduction, or results section
- Key findings in their own words
- A study summary or internal report
Also ask: Who is the primary audience?
mixed(default) — all teams listedclinical— clinicians, medical staffwet-lab— bench scientists, experimentalistsbioinformatics— computational scientists, data analystsproduct— product managers, translational teamsmanagement— leadership, funders, executives
If unspecified, use mixed and include all relevant audience bullets.
Step 2 — Extract Core Structure
Before writing, internally map the input to these five elements:
| Element | What to find | |---|---| | Study goal | Why was this done? What problem does it address? | | System / population | What was studied? (patients, cells, datasets, samples…) | | Main finding | What did the data show? Be specific — avoid vague positives. | | Evidence boundary | What can this support? What remains uncertain or untested? | | Next action | What should each team know or do because of this? |
If any element is missing from the input, note it in the output and invite the user to fill in the gap.
Step 3 — Write the Lay Summary
Use the output template in assets/output-template.md.
Writing principles:
- No unexplained acronyms — define on first use or remove
- Evidence boundary must be explicit: distinguish finding from interpretation
- Each audience bullet should be actionable, not just descriptive
- Quantify findings where possible ("3-fold higher", "in 4 of 6 subtypes")
- The summary must stand alone without access to the original paper
For audience-specific language guidance, read references/audience-guide.md.
Step 4 — Quality Check
Before delivering output, verify:
- [ ] No naked jargon or undefined acronyms
- [ ] Finding is accurate — not overstated, not undersold
- [ ] Evidence boundary is clearly hedged
- [ ] Each audience bullet is actionable
- [ ] Summary reads cleanly to someone with no domain knowledge
If a check fails, revise before presenting.
References
assets/output-template.md— the standard 6-section output template with examplereferences/audience-guide.md— language and framing guidance per audience type
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: aipoch
- Source: aipoch/medical-research-skills
- License: MIT
- Homepage: https://aipoch.com/agent-skills
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.