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Automated security scan — not a certification.
by Light0305
A research workflow skill pack connecting literature, experiments, figures, writing, memory and project coordination.
# Add to your Claude Code skills
git clone https://github.com/Light0305/Light-ScienceGuides for using ai agents skills like Light-Science.
Last scanned: 11/7/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-11-07T10:53:37.767Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how Light-Science compares with popular alternatives.
Light-Science is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Light0305. A research workflow skill pack connecting literature, experiments, figures, writing, memory and project coordination. It has 877 GitHub stars.
Yes. Light-Science passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Light0305/Light-Science" and add it to your Claude Code skills directory (see the Installation section above).
Light-Science is primarily written in Python. It is open-source under Light0305 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Light-Science against similar tools.
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Automated security scan — not a certification.
Light-Science (formerly Light-skills) is a research skills pack for AI agents. It connects literature, ideation, design, experiments, analysis, journal selection, writing, figures and review. Project memory keeps the reasons behind research decisions, and a leader skill coordinates the next step, so every revision can be traced back to a concrete source, version and artifact.
Start from scratch, or bring an existing paper, dataset, codebase or set of reviewer comments and go straight to the stage you need.
The skill instructions are written in Chinese, and several skills reply in Chinese by default. Ask your agent to answer in English if you prefer.
Send this to your agent:
Install the research skills from https://github.com/Light0305/Light-Science.
Ask me first whether to install globally or for the current project, then install the skills I need from the skills table in the README.
Windows, in PowerShell:
npx.cmd skills@latest add Light0305/Light-Science
npx.cmd skills@latest add https://github.com/Light0305/Light-Science/tree/master/light-chart/skills/light-chart
macOS / Linux:
npx skills@latest add Light0305/Light-Science
npx skills@latest add https://github.com/Light0305/Light-Science/tree/master/light-chart/skills/light-chart
The first command lists the top-level skills; the second adds Chart, which lives in a nested folder. Pick the same agent and install scope both times, otherwise Chart ends up somewhere else. The Diagram entry keeps its four sub-skill folders, and each sub-skill can also be installed on its own. For a project-level install, change into the target project first. You can also install a single skill:
npx skills@latest add Light0305/Light-Science --skill light-write
More detail on install locations, dependencies and invocation is in the installation guide (Chinese).
Use light-leader to move this project forward: I want to study <specific question>, and I already have <papers/data/code>.
The project lives at <project path>, and the goal is <a paper / a software copyright and related outputs>.
First read the existing project memory and the actual materials, find the most pressing problem, then complete the step that can be done now.
To continue an existing project:
Continue <project path>. Read Memory and the latest handoff first, then check the adopted versions of the manuscript, figures and analyses.
This round, handle <research question or reviewer comments>, and write the results, the reasons for each decision and the next step back into the project.
Replace everything in angle brackets before sending. For a focused task, just say "use light-chart to plot this data" or "use light-write to revise this section".
15 main skills plus 4 dedicated Diagram entries: 19 in total.
| Skill | What it does | Main output |
|---|---|---|
| light-literature / literature-search | Literature search, screening, full-text retrieval, close reading, translation, citation checking and evidence synthesis | Literature/ |
| light-idea | Proposes research questions from evidence; compares candidate ideas, competing explanations and minimal discriminating experiments | Idea/ |
| light-design | Turns an idea into an executable, discriminating plan with controls, metrics, budget and an analysis plan | Design/ |
| light-experiment | Runs computational experiments, simulations or benchmarks; keeps configs, environments, logs and raw results | Experiment/ |
| light-analysis | Analyzes results, errors, uncertainty and alternative explanations; decides whether to run more experiments, revise the design or go back to the question | Analysis/ |
| light-crawl | Collects website or app data on demand, keeping access conditions, failure records and data provenance | Acquisition/ |
| light-journal-select | Chooses journals from the actual contribution and evidence, checks official requirements, and writes a recommendation and a writing brief | Journal/ |
| light-write | Drafting, restructuring, polishing, translation and consistency checks for papers; delivers LaTeX plus a compile-checked PDF by default | Write/ |
| light-diagram | Method and mechanism figures: intent, references, prompts, image generation and editable reconstruction | Figure/Methods/, Figure/Mechanisms/ |
| light-chart | Python / R scientific charts, multi-panel figures and paper tables from real data | Figure/Data/ |
| light-review | Internal pre-submission review: checks figures, text and evidence, locates problems and tracks re-checks | Review/ |
| light-patent | Prepares invention disclosures, claims, specifications, abstracts and drawings from real technical material | Patent/ |
| light-software-copyright | Prepares the three DOCX files of a Chinese software copyright filing (feature description, source code, user manual) from real software | SoftwareCopyright/ |
| light-memory | Keeps sourced decisions, failure lessons and constraints; restores context and flags changed or conflicting sources | Memory/ |
| light-leader | Combines memory with the actual materials to coordinate skills, research backtracking, paper revisions and recovery after interruptions | Workflow/ when needed |
The Diagram entries can be used on their own or called by the main entry as needed:
| Skill | Delivers |
|---|---|
| light-diagram-prompt | Scientific brief, structural blueprint, image-generation prompts and checkpoints |
| light-illustrator-diagram | Native Illustrator .ai plus real exports |
| light-powerpoint-diagram | Editable .pptx plus real exports |
| light-visio-diagram | Native Visio .vsdx plus real exports |
light-analysiswas namedlight-anaysisin v2.0.0 and earlier. If you installed the old name, reinstall it under the new one; old project records that mentionlight-anaysisrefer to the same skill.
Research is allowed to go backwards: analysis may expose problems in execution, a weak design or a gap in the research question itself, and review may call for more analysis, more experiments or a different target journal. Data acquisition, patents and software copyrights join when the project needs them, and can be prepared early when publication timing matters.
The leader restores, then verifies, then acts. It reads the relevant project memory and handoffs, goes back to the papers, code, run results and adopted figures to check the actual state, and decides which skill takes the next step. Only when several steps need coordinating does it keep goals, dependencies, progress, acceptance criteria and a recovery entry in Workflow/.
Memory lets research pick up where it left off. Why the direction changed, which plan failed and which constraints still hold are all recorded with their sources. When a source changes or sources conflict, it reminds you to re-verify; the scientific facts themselves stay in each stage's original files. Real progress from research or revisions is written back to memory, and the next round starts from there.
flowchart TB
Leader[light-leader · restore and coordinate] <--> Memory[light-memory · sourced project memory]
Leader -. dispatch by goal and actual state .-> Research
Leader -. dispatch by review and delivery needs .-> Manuscript
subgraph Research[Research loop]
direction LR
L[Literature] --> I[Idea] --> D[Design] --> E[Experiment] --> A[Analysis]
A --> I
A --> D
A --> E
I --> L
end
C[Acquisition on demand] -. sources and data .-> Resear