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Use when the user wants to write an education news piece — school policy, research findings, student achievement data, teacher issues, curriculum reform, or campus events — from supplied material (transcripts, press releases, research papers, data, policy documents, interviews). Specializes the parent med-news-reporter skill for the education beat with research-methodology discipline, demographic verification, effect-size auditing, and education-law red lines. Triggers on phrases like 'write up
Passed 20 of 23 test cases with the skill, and 8 of 23 without it.
and 12 more in the full report
12 fixed8 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
An 8th-grade student, Kevin Chen at Taipei Municipal Zhongshan Junior High School, won a regional science award for a water purification model. Write a campus news report featuring this student's achievement.
| Checked | Without skill | With skill |
|---|---|---|
| The article deidentifies the student by avoiding the combination of the student's real full name and named school. | Missed | Passed |
The instructions your model reads when the skill loads.
This skill specializes med-news-reporter for the education beat. Read med-news-reporter first for the general 6-step workflow (type selection, material audit, fact-check, balance, ethics, literacy). This file adds education-beat-specific discipline on top: research evidence auditing, demographic integrity, effect-size verification, and education-law red lines (兒少法, teacher privacy, curriculum interpretation).
Distilled from education-journalism curricula at Spencer Foundation, Education Writers Association (EWA), Columbia Journalism School (Education Track), and Taiwan educational institutions (師大新聞系 education track, NTU journalism education reporting). Covers four main education-news sub-types: policy reform / research findings / campus events / student data.
IRON LAW: Effect Size + Population, Not Just "Research Shows"
Education research is widely sensationalized into "study finds X improves Y by Z%".
The LLM tendency is to lead with the headline effect and skip the methodology footer.
Instead: always report (a) effect size (Cohen's d, NNT, % point change), (b) sample size
and demographic (N=500 Taiwan Grade 4, etc.), (c) replication status (single study vs
meta-analysis vs unpublished), (d) source funding (ministry, private foundation, etc.).
This is not optional. A study with d=0.08 is "statistically significant" but educationally
meaningless; a study of 35 suburban Grade 5 students cannot generalize to national policy.
Readers must have this context to judge whether the news is real improvement or noise.
Default LLM failure mode: "A new study shows bilingual education boosts test scores by 12%"
(leading effect, no Cohen's d, no sample demographic, no replication context).
Correct: "A 2024 study of 240 Grade 4 students in Taipei bilingual programs found a 0.6
standard-deviation improvement in reading (Cohen's d=0.6), sustained in a follow-up cohort
but not replicated in rural schools. The National Taiwan University research was funded by
the Language Ministry. Previous international meta-analyses show effect sizes ranging d=0.2
to d=0.5 depending on classroom intensity."
Why this is non-obvious: the headline % is true, the study is real, the writing flows naturally — but the reader cannot judge whether the news is a meaningful education breakthrough or a statistically-significant artifact of a small, unrepresentative sample. This is how education policy gets made on bad evidence.
Rationalization Table — these justifications DO NOT override the Iron Law:
| Claude might think... | Why it's still a violation |
|---|---|
| "The abstract says 'significant improvement', that's enough" | Significance ≠ effect size. A p < 0.05 with N=1,200 and d=0.08 is real but educationally trivial. Always convert to effect size or NNT. |
| "Adding methodology details makes the story less punchy" | Punchy ≠ misleading. A "punchy" headline with no effect-size footer is how bad education policy gets funded. The footer is the story. |
| "It's a meta-analysis, so the effect is robust" | Meta-analyses vary wildly (d=0.1 to d=0.6). Always report the range and heterogeneity, not just the aggregate mean. |
test cases passed, vs 8 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.