Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
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 Galaxy-Dawn/claude-scholar --skill results-reportgit clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote 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/galaxy-dawn/claude-scholar/results-report)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/results-report"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/results-report/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/galaxy-dawn/claude-scholar/results-report"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/results-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00078 | $0.01408 |
| Opus 5.5 | $0.00031 | $0.00563 |
| Sonnet 5.5 | $0.00016 | $0.00282 |
| Haiku 4.5 | $0.00008 | $0.00141 |
Grade A, and why
results-report 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 1mo 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Results Report
Write the complete post-experiment summary report after analysis artifacts are ready.
This skill is for the stage after results-analysis.
Role boundary
results-analysis does
- strict statistics,
- real figures,
- figure interpretation scaffolding,
- stats appendix.
results-report does
- complete experiment wrap-up report,
- decision-oriented narrative,
- figure-by-figure interpretation inside a coherent structure,
- limitations, failure cases, and next actions,
- Obsidian write-back into
Results/Reports/.
When the task is to create or redesign paper-ready figures/tables themselves, rely on publication-chart-skill instead of expanding results-report into figure/table production.
Do not replace strict analysis with confident prose. If the analysis bundle is missing, first identify the blocker and request or produce the missing bundle.
Hard gate: do not write a complete report when analysis-report.md, stats-appendix.md, figure-catalog.md, provenance, or the primary comparison question is missing. In that case, write only a blocker summary and route back to results-analysis.
Default output
The default report is an internal research report, not manuscript prose.
It should be named as:
YYYY-MM-DD--{experiment-line}--r{round}--{purpose}.md
Example:
2026-03-18--freezing--r03--transfer-summary.md2026-03-18--contrastive-adversarial--r02--ablation-report.md
The note title should be:
{Experiment Line} / Round {N} / {Purpose} / {YYYY-MM-DD}
Read references/report-naming.md before finalizing the filename or note title.
Required frontmatter
---
type: results-report
date: 2026-03-18
experiment_line: freezing
round: 3
purpose: transfer-summary
status: active
source_artifacts:
- analysis-output/analysis-report.md
- analysis-output/stats-appendix.md
linked_experiments:
- Experiments/Freezing-Study.md
linked_results:
- Results/Freezing-vs-Adapter.md
---
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 187 lines · 78 tokens per session scan A d35cdde436ba
results-report is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,912 stars, last pushed 8d ago), licensed MIT. It adds 78 tokens to every session and 1,408 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
kami-deck
A lab-meeting deck on gut-microbiome links to sleep quality — the design, the results, the caveats, and the next experiment. Built as a decision-grade academic research deck for lab group, PI.
design-experiment
Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness designs are modes, not strategy nodes.
bias-detection
Assess systematic biases in the evidence body — publication bias, reporting bias, and selective outcome reporting. Budget: 40 studies, 40 effect sizes, 40 web searches.
aeja-robustness
Use when an American Economic Journal: Applied Economics (AEJ: Applied) manuscript's headline estimate must be shown to survive specification, sample, and inference choices before submission or in an R&R. Builds the robustness suite a sophisticated referee expects; it does not establish the primary identification…
aeja-workflow
Use when deciding which aeja- sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Applied Economics (AEJ: Applied) submission. Routes — it does not replace — the specialized skills.
aejmac-workflow
Use when deciding which aejmac- sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Macroeconomics (AEJ: Macro) submission. Routes — it does not replace — the specialized skills.