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 beita6969/ScienceClaw --skill education-researchgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/education-research)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/education-research"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/education-research/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/beita6969/scienceclaw/education-research"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/education-research.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 | $0.00046 | $0.00842 |
| Opus 5 | $0.00023 | $0.00421 |
| Sonnet 5 | $0.00009 | $0.00168 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
education-research 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Pedagogical methods, teaching strategies, instructional design
- Learning analytics, student performance data, LMS data
- Assessment design, test validity, reliability, item analysis
- Curriculum development, learning objectives, Bloom's taxonomy
- Educational technology, e-learning, blended learning, MOOCs
- Educational interventions, quasi-experimental designs in education
- Student engagement, motivation, self-regulated learning
Step-by-Step Methodology
- Define the research question - Specify the educational context (K-12, higher education, professional development). Identify the intervention, outcome measures, and comparison conditions. Frame using established educational theory (constructivism, connectivism, cognitive load theory).
- Study design - Select appropriate design: RCT (gold standard but often impractical), quasi-experimental (difference-in-differences, regression discontinuity), or mixed methods. Address common challenges: nested data (students within classrooms), selection bias, contamination between groups.
- Assessment development - Define learning objectives using Bloom's taxonomy (remember, understand, apply, analyze, evaluate, create). Develop assessment items aligned with objectives. Compute reliability (Cronbach's alpha, test-retest, inter-rater). Conduct item analysis (difficulty, discrimination index).
- Data collection - Gather quantitative data (test scores, grades, completion rates, time-on-task from LMS logs) and qualitative data (surveys, interviews, observations, think-alouds). Ensure IRB approval for human subjects research.
- Multilevel analysis - Use hierarchical linear modeling (HLM) to account for nested data structure (students within classrooms within schools). Report ICC (intraclass correlation) to justify multilevel approach. Include relevant covariates (prior achievement, demographics).
- Effect size and practical significance - Report Cohen's d or Hedges' g for group comparisons. Use standards for education research: d = 0.2 (small), 0.4 (medium), 0.6 (large). Translate to months of learning gain for K-12 contexts (What Works Clearinghouse approach).
- Evidence synthesis - Situate findings within existing evidence base. Reference systematic reviews (What Works Clearinghouse, EPPI-Centre, Campbell Collaboration). Discuss generalizability, implementation fidelity, and scalability.
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 · 55 lines · 46 tokens per session scan A b64dc6253156
education-research is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 842 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 2026-09-03.
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