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-analysisgit 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-analysis)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/results-analysis"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/results-analysis/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-analysis"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/results-analysis.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 | $0.00068 | $0.01970 |
| Opus 5 | $0.00034 | $0.00985 |
| Sonnet 5 | $0.00014 | $0.00394 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade A, and why
results-analysis 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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Results Analysis
Run strict, evidence-first experimental analysis for ML/AI research.
Use this skill to produce a strict analysis bundle:
analysis-report.mdstats-appendix.mdfigure-catalog.mdfigures/
When the user asks for review, audit, no-write, dry-run, or when inputs are incomplete, use read-only audit mode instead of producing files or figures. In that mode, output only valid/invalid statistics, blockers, claim candidates, and what evidence is missing. If invoked by /analyze-results, the command layer may write a blocker summary, but this skill should not create figures, reports, or polished conclusions from incomplete evidence.
Do not use this skill to draft a paper Results section or a full experiment wrap-up report. Those belong to ml-paper-writing or results-report.
Core contract
This skill is responsible for
- validating experiment artifacts and comparison units,
- running rigorous descriptive and inferential statistics,
- generating real scientific figures when data/logs are available,
- writing figure purposes, caption requirements, and interpretation checklists,
- surfacing limits, blockers, and missing evidence explicitly.
This skill is not responsible for
- paper-ready
Resultsprose, - manuscript narrative polishing,
- paper-ready figure/table packaging with
pubfig/pubtab, - project-level experiment retrospectives.
If the user wants the complete post-experiment summary report, hand off to results-report after this bundle is ready. If the user wants publication-grade figures/tables, export parameters, publication QA, or figure/table redesign, hand off to publication-chart-skill.
Non-negotiable quality bar
- Prefer real figures over figure specs. If the data can be read, generate real figures. Do not stop at “recommended visualization”. Exception: in read-only audit mode, do not generate figures; describe what figure would be valid after evidence is complete.
- Never fabricate statistics. If sample size, seeds, or raw metrics are missing, state the blocker clearly.
- Report complete statistics. Do not report only best scores or only p-values.
- Interpret every main figure. Every major figure must have purpose, caption requirements, and post-figure interpretation notes.
- Separate evidence from prose. This skill produces analysis artifacts; it does not write manuscript sections.
What ships with it
10 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.
- examples/example-analysis-report.md 2.2 KB
- examples/example-figure-catalog.md 1.3 KB
- examples/example-stats-appendix.md 1.2 KB
- references/analysis-depth.md 844 B
- references/common-pitfalls.md 998 B
- references/figure-interpretation.md 939 B
- references/statistical-methods.md 16 KB
- references/statistical-reporting.md 1.1 KB
- references/visualization-best-practices.md 6.4 KB
- USAGE.md 2.9 KB
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 · 253 lines · 68 tokens per session scan A 2232f74d141f
results-analysis is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,431 stars, last pushed 16d ago), licensed MIT. It adds 68 tokens to every session and 1,970 once invoked, about $0.0003 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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