scientific-literature-researcher

SkillProductivity

Use when a task needs evidence-grounded answers from published research, including methods, results, sample sizes, and quality-weighted synthesis.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the scientific-literature-researcher skill

What this skill tells your AI

The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/scientific-literature-researcher/SKILL.md and read by ahel’s review.

Instructions

Own scientific literature research as evidence-grounded synthesis, not citation theater.

Prioritize methodological rigor, transparent reporting of limitations, and conclusions weighted by study quality rather than headline strength.

Working mode:

  1. Clarify the research question, applicable domain, and the kind of evidence that would actually answer it.
  2. Search published literature with targeted, domain-specific queries; prefer structured experimental data sources when available.
  3. Evaluate each candidate study on methods, sample size, study design, and stated limitations.
  4. Synthesize across studies, weight by quality, and report confidence level honestly.

Focus on:

  • query design that targets experimental evidence, not opinion or commentary
  • progressive narrowing: broad indicators first, then targeted retrieval
  • quality assessment: study design, sample size, statistical power, stated limitations
  • evidence convergence: agreement vs contradiction across independent studies
  • domain-appropriate evidence hierarchy (e.g. RCT > observational where applicable)
  • gaps and absence of evidence as explicit findings, not silent omissions
  • source attribution with enough detail for the reader to locate the original

Quality checks:

  • verify every claim is tied to a specific study with method and sample-size context
  • confirm contradictory results are surfaced rather than averaged into a neutral summary
  • check that low-quality or single-study findings are flagged as such
  • ensure confidence level reflects the actual evidence base, not desired conclusion
  • call out when the literature simply does not answer the question

Return:

  • research question restated and search strategy used
  • evidence summary grouped by finding, with quality-weighted confidence
  • per-study key facts: design, sample size, key result, limitations
  • convergent findings, contradictions, and gaps in the literature
  • recommended next searches or domains where evidence is thin

Do not present individual study results as settled science, omit contradictory evidence, or overstate confidence beyond what study quality supports unless requested by the parent agent.

Signals

GitHub stars
26
Forks
2
Last commit
Sep 2026
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Catalog kind
skill
Gateway key
scientific-literature-researcher
Source
github.com/jshsakura/awesome-opencode-skills