Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Use when writing a medical or health news story — clinical research breakthroughs, public health alerts, drug approvals, epidemiology, health policy, patient stories, risk communication — from research papers, press releases, health authority statements, or interviews. Specializes the med-news-reporter workflow for health-beat discipline: relative risk framing, absolute baseline inclusion, evidence-hierarchy verification, deidentification protocol, and WHO suicide-reporting compliance. Triggers
.claude/skills/asgard-ai-platform-med-health/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 211% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 125% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 105% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 120% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 246% | 0% |
> This skill specializes med-news-reporter for the medical/health beat. Read med-news-reporter first for the general 6-step workflow (type selection, material audit, fact-check, balance, ethics, literacy). This file adds health-specific discipline on top.
Distilled from health-journalism curricula at Stanford Medicine+Muse, Johns Hopkins SFDH, AHCJ (Association of Health Care Journalists), Columbia Mailman, NTU Public Health, and Taiwan health-media ethics standards. Covers five sub-types: research breakthroughs / public-health alerts / drug approval / health policy / patient stories. Core challenge: translating statistical evidence for public understanding without misrepresenting risk or false certainty.
IRON LAW: Relative Risk Without Absolute Risk Is Misleading
Every medical claim in the form "X% increase/decrease in risk" MUST cite
absolute baseline numbers: baseline incidence, NNT (Number Needed to Treat),
absolute risk reduction, or absolute risk change. "50% reduction in risk of
heart attack" is meaningless without "from 4 in 1000 to 2 in 1000 per year".
LLM default: lead with the relative risk (sounds dramatic), omit baseline.
Readers then overestimate the clinical significance. Override that default
by naming the denominator first, then the percentage.Why this is non-obvious: "50% reduction" sounds much more impactful than "2 fewer heart attacks per 1000 per year", yet both describe the same result. Research-to-media translation routinely inverts this — the press release says "50% reduction", the outlet runs that number, and readers assume a larger clinical effect than evidence supports. This is the single most common source of health-news overclaim.
Rationalization Table — these justifications DO NOT override the Iron Law:
| Claude might think... | Why it's still a violation | |---|---| | "'50% reduction' is the research result, I'll just quote it" | Quoting a relative-risk figure without the absolute baseline is relaying an incomplete fact. The journal paper has the baseline; the press release usually does not. Cite both or cite neither + flag. | | "The baseline is in the methods section, readers can look it up" | Readers will not. The article is the only context they read. Omitting it is misleading by omission, not just incomplete. | | "Adding the absolute number makes the story less dramatic" | That is the point. Accuracy is not a bug. If the absolute effect is small, the reader deserves to know. | | "NNT is too technical for general audiences" | True, and it's also the clearest way to show clinical significance. Use NNT in a side sentence ('meaning doctors would need to treat about 500 people to prevent one case'). Not optional. | | "The researcher said 'statistically significant'—that's the main story" | Statistically significant ≠ clinically significant. A study of 100,000 people can show a 0.5% effect as "significant" if it's real. Report both p-value and effect size. |
Trigger conditions:
Input signals:
When NOT to use:
pr-press-release.pr-*.Read or have already loaded med-news-reporter for: material audit, fact-checking, source-strength tagging, balance principle, media-ethics check, media-literacy self-check. Do not re-implement those steps here. This file specializes Steps 1, 2, 3, and adds health-specific Step 7 (Evidence Hierarchy & Risk Framing Audit).
| Sub-type | Signals | Sub-template focus | |----------|---------|--------------------| | Research breakthrough | Journal paper, pre-print, press release from university/NIH | Evidence level check; RR + AR framing; replication status | | Public health alert | CDC alert, 衛福部 advisory, WHO statement, disease outbreak | Absolute numbers (cases, deaths); transmission risk; at-risk population; response guidance | | Drug approval | FDA/食藥署 approval, Phase III completion, clinical trial results | Trial design rigor; efficacy + side-effect rate; NNT; cost/access; alternative treatments | | Health policy | Coverage decision, vaccine recommendation, screening guideline, regulation | Policy rationale; affected population; evidence basis; expert consensus; dissenting opinion | | Patient story | Interview, testimonial, case narrative | De-identification protocol; generalizability limits; attribution; expert context |
If material spans sub-types (e.g. a policy change triggered by a study), classify by the primary news driver.
Every health claim must carry evidence-level tag at first mention:
Evidence Hierarchy (strongest → weakest):
1. Meta-analysis / systematic review of RCTs
2. Large RCT (n > 500)
3. Small RCT (n < 500)
4. Cohort study / case-control study
5. Case series / case report
6. Expert opinion / editorials
7. Anecdote / single patient storyBad tagging: 「新研究表示...」(which study? what strength?) Good tagging: 「今年發表在 Lancet 的一項 1,200 人隨機對照試驗表示...」or 「基於個案報告(證據等級 5)...但尚未進行人體試驗」
Source tier (extends med-news-reporter):
| Tier | Examples | Treatment | |------|----------|-----------| | Government health authority | CDC, 衛福部、疾管署、食藥署、WHO | Direct citation; highest credibility tier | | Peer-reviewed journal | Lancet, JAMA, BMJ, Nature Medicine, 台灣醫學會期刊 | Always cite journal name + DOI; include publication date | | Preprint / not yet peer-reviewed | medRxiv, bioRxiv | Must flag as "not yet peer-reviewed"; requires editor review before publication | | University press release | Without access to actual paper | Treat as Tier 2.5; verify against journal preprint / abstract | | Single researcher quote | Without published evidence | Tier 4; acceptable only as "expert opinion" with explicit caveat | | Pharmaceutical company | Clinical trial sponsor | Tier 3–4; always disclose funding source; cross-verify against independent data when possible | | Patient anecdote | Interview, testimonial, Facebook post | Tier 7; only acceptable as illustrative narrative, never as evidence |
Beyond med-news-reporter's general ethics check, add:
Before output, apply:
Use the med-news-reporter base format, with health-specific additions to the meta footer:
markdown[Headline / sub-headline / body paragraphs per med-news-reporter] --- **稿件類型**: 醫學研究報導 / 公衛警訊 / 藥品核准 / 健康政策 / 患者故事 **字數**: approx. XXX **消息來源層級**: 政府公衛機構 N / 同儕評審期刊 N / 預印本 N / 企業新聞稿 N / 專家意見 N / 患者訪談 N **醫學證據稽核**: - 每項醫學宣稱之證據等級: ✅ / ⚠️ (列出未標的) - 相對風險 + 絕對風險配對: ✅ / ⚠️ (列出缺項: RR 未伴絕對值、NNT、基礎風險) - 單一研究 vs 系統性評論: ✅ / N/A / ⚠️ - 95% CI / 不確定性表述: ✅ / ⚠️ (列出未含的宣稱) **患者隱私檢核**: - 去識別化: ✅ / ⚠️ (列出仍可追蹤身份的資訊) - 同意書揭露: ✅ / N/A / ⚠️ **WHO 自殺守則**: - 適用: N/A / ✅ (已遵守) / ❌ (違反項目) **利益衝突揭露**: - 資金來源: ✅ / N/A / ⚠️ (列出未揭露的利益相關) **待查證事項**: ... **倫理 / 識讀檢核摘要**: 〔交給 med-news-reporter 的 Step 4-5 footer〕
See examples/ directory for:
sample_input.md — realistic health-news source material (clinical study press release + health authority statement + medical society response + patient anecdote)sample_output.md — produced piece + meta footer + skill-trace explanation| File | Purpose | When to read | |------|---------|--------------| | references/sources_and_beats.md | 台灣衛生醫療消息來源、機構、官方資料庫 | Step 2 source vetting | | references/glossary.md | 醫學統計、流行病學、臨床試驗術語對照 | When unfamiliar medical terminology appears | | references/ethics_and_law.md | PDPA / 醫療法 §72 / 醫療廣告法 / 自殺守則 | Step 3 risk check | | references/medical_evidence_reading.md | 證據等級金字塔、相對風險誤導、P-hacking | Step 1/4 evidence hierarchy | | references/risk_communication.md | 風險溝通原則、絕對 vs 相對、不確定性表述 | Step 4 risk framing |
Related skills:
med-news-reporter — general news workflow (this skill specializes it)med-political — health policy & regulatory newsstat-hypothesis-testing — deeper statistical literacy on RCTs and meta-analysesstat-causal-inference — for causation claims in observational studieshum-source-criticism — source vetting frameworksreferences/medical_evidence_reading.md, but for deep methodological critique use stat-hypothesis-testing or grad-survey-design.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 21,406 | 58,017 | +171% | 1 | 1 | 0% | 3,035 | 8,289 | +173% | 0 | 0 | — |
case-02 | fail→pass | 17,270 | 26,504 | +53% | 1 | 1 | 0% | 2,650 | 8,233 | +211% | 0 | 0 | — |
case-03 | fail→pass | 21,004 | 22,992 | +9% | 1 | 1 | 0% | 3,323 | 7,468 | +125% | 0 | 0 | — |
case-04 | pass→pass | 10,804 | 5,168 | -52% | 1 | 1 | 0% | 1,719 | 4,888 | +184% | 0 | 0 | — |
case-05 | fail→pass | 15,820 | 8,991 | -43% | 1 | 1 | 0% | 2,677 | 5,483 | +105% | 0 | 0 | — |
case-06 | fail→pass | 16,732 | 8,665 | -48% | 1 | 1 | 0% | 2,429 | 5,347 | +120% | 0 | 0 | — |
case-07 | fail→fail | 16,069 | 24,156 | +50% | 1 | 1 | 0% | 2,380 | 7,608 | +220% | 0 | 0 | — |
case-08 | fail→pass | 14,606 | 19,232 | +32% | 1 | 1 | 0% | 1,970 | 6,825 | +246% | 0 | 0 | — |
case-09 | fail→pass | 15,564 | 24,673 | +59% | 1 | 1 | 0% | 2,193 | 7,350 | +235% | 0 | 0 | — |
case-10 | fail→fail | 17,652 | 18,790 | +6% | 1 | 1 | 0% | 2,331 | 6,938 | +198% | 0 | 0 | — |
case-11 | fail→pass | 13,361 | 19,016 | +42% | 1 | 1 | 0% | 2,041 | 6,761 | +231% | 0 | 0 | — |
case-12 | fail→pass | 15,239 | 19,837 | +30% | 1 | 1 | 0% | 2,187 | 7,342 | +236% | 0 | 0 | — |
case-13 | pass→pass | 13,751 | 21,669 | +58% | 1 | 1 | 0% | 1,899 | 7,097 | +274% | 0 | 0 | — |
case-14 | fail→pass | 11,641 | 22,194 | +91% | 1 | 1 | 0% | 1,979 | 7,514 | +280% | 0 | 0 | — |
case-15 | fail→pass | 12,037 | 22,887 | +90% | 1 | 1 | 0% | 1,907 | 7,654 | +301% | 0 | 0 | — |
case-16 | fail→pass | 14,251 | 21,950 | +54% | 1 | 1 | 0% | 2,351 | 7,044 | +200% | 0 | 0 | — |
case-17 | pass→pass | 12,713 | 23,036 | +81% | 1 | 1 | 0% | 2,008 | 7,320 | +265% | 0 | 0 | — |
case-18 | fail→pass | 13,241 | 22,969 | +73% | 1 | 1 | 0% | 2,198 | 7,892 | +259% | 0 | 0 | — |
case-19 | fail→pass | 18,228 | 26,941 | +48% | 1 | 1 | 0% | 3,171 | 8,248 | +160% | 0 | 0 | — |
case-20 | fail→pass | 15,167 | 22,843 | +51% | 1 | 1 | 0% | 2,311 | 7,395 | +220% | 0 | 0 | — |
case-21 | pass→pass | 13,606 | 21,987 | +62% | 1 | 1 | 0% | 2,069 | 7,518 | +263% | 0 | 0 | — |
case-22 | fail→pass | 14,867 | 15,899 | +7% | 1 | 1 | 0% | 2,446 | 6,641 | +172% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +68 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.