Light Skills: an AI skill pack for research papers, competitions and innovation projects

What Light Skills actually solves for a research project

The pitch is narrow and worth stating plainly: an AI agent asked to "write my paper" will invent citations, invent a figure pipeline, and invent a result. Light Skills is a set of 23 skills that constrain what the agent does at each stage of a research or innovation project, from a bare direction to a submittable artifact. The README frames the scope as public, general and domain-agnostic, and the skills table backs that up: literature search, idea critique, experiment planning, data engineering, result analysis, paper writing, citation checking, LaTeX typesetting, venue matching, figures, frontend demos, patents and software copyright.

The target user is a graduate student, a competition team, or a small lab that already works with an agent such as Claude Code, Codex or OpenCode. The README's own entry table is organized by what you currently have: only a direction, an idea, existing project files, a literature question, an experiment to design, a figure to draw, a paper to write, a submission to typeset, or a demo interface to build. That is a reasonable map of where research work stalls.

The design rules are the interesting part. The README states four: read files, data, logs and paper sources before deciding the next step; mark unknown when a fact, DOI, link, journal rule or software version cannot be checked; generate paper and data figures programmatically in Python or R; and ask the user to confirm at key points such as topic selection, novelty, evidence strength, submission target and whether to keep investing. It also states the public version requires no MCP server and no local database, with current information checked at task time.

How the skills are wired: SKILL.md files, a bootstrap script and a shared layer

The repository layout is the mechanism. Top-level entries include skills/, scripts/, _shared/, docs/, examples/, projects/, assets/, plus AGENTS.md, CHANGELOG.md, CONTRIBUTING.md and SECURITY.md. Each skill lives in its own directory under skills/, and per the README's install comments the agent-facing file is SKILL.md inside that directory, for example .claude/skills/<skill>/SKILL.md for Claude Code, .agents/skills for Codex and .opencode/skills/<skill>/SKILL.md for OpenCode.

scripts/bootstrap_agent_skills.py is the installer. It takes --targets, --mode, --force and --check-only, and it copies or links the skills into the directory each agent reads. The _shared/ directory suggests common material that individual skills reference rather than duplicate, though the README excerpt does not spell out its contents.

The skills themselves are grouped into five modules in the README: orchestration and continuity (light-orchestrator, light-memory-pm, light-file-reading, light-project-structure), ideas and literature (light-literature-search, light-idea-generation, light-idea-critique, light-research-plan), data and experiments (light-data-engineering, light-experiment-coding, light-result-analysis), paper delivery (light-paper-writing, light-citation, light-consistency, light-typesetting, light-venue-matching, light-review-rebuttal), and figures and presentation (light-figure, light-frontend-design, light-system-design), plus integrity and translation-to-output (light-research-ethics, light-patent-disclosure, light-software-copyright).

light-orchestrator is the entry point: it reads the task, asks for missing information, selects a chain of skills and sets user confirmation points. light-memory-pm is the continuity layer, and the README is explicit that it keeps a project card, handoff card and decision log without writing private memory into a public repository. That separation is a real design decision, not a slogan: the ledger records goals, decisions, artifacts, unverified claims and next steps so a later conversation can resume.

Installing Light Skills and running a first literature-review pass

The README gives PowerShell commands, so this walkthrough stays in PowerShell. Start by cloning and entering the repository. The PYTHONUTF8 setting is recommended before running Python on Windows.

git clone https://github.com/Light0305/Light-skills.git
cd Light-skills
$env:PYTHONUTF8="1"

Next, install the skills for the agent you use. The README shows a project-level install for each of the three supported agents, writing into the repository's own agent directory. This is the Codex variant, which targets .agents/skills.

$env:PYTHONUTF8="1"
python scripts\bootstrap_agent_skills.py --targets agents --mode auto --force

For Claude Code the same script is called with --targets claude, and for OpenCode with --targets opencode. The README also documents a global install that copies the skills directory into $HOME\.agents\skills, $HOME\.claude\skills or $HOME\.config\opencode\skills respectively.

Verify what was installed before trusting the setup. The check-only flag reports the state without writing.

$env:PYTHONUTF8="1"
python scripts\bootstrap_agent_skills.py --check-only

With the skills in place, the README's entry table gives copy-ready prompts. For a literature pass on an existing question, the documented invocation is the light-literature-search skill, which the README says produces a search strategy, keyword expansion, an evidence map and a boundary for related work.

$light-literature-search 围绕这个问题做检索策略、关键词扩展、证据地图和相关工作边界。

Note the prompt prefix: the README uses $light-<skill> for Codex-style invocation and /light-orchestrator for the orchestrator. The README's examples are written in Simplified Chinese, so an English-speaking user should expect to rewrite the prompt body while keeping the skill name.

Optional toolchains are separate. LaTeX is installed through winget and verified with latexmk, pdflatex, xelatex and biber.

winget install --id MiKTeX.MiKTeX --accept-package-agreements --accept-source-agreements
latexmk -v

R is installed the same way, then ggplot2 and scales, then detected by the figure skill's own script.

winget install --id RProject.R --accept-package-agreements --accept-source-agreements
Rscript -e "install.packages(c('ggplot2','scales'), repos='https://cloud.r-project.org')"
python skills\light-figure\scripts\r_ggplot.py --detect

The README says light-typesetting supports latexmk, pdfLaTeX, XeLaTeX, LuaLaTeX, BibTeX and Biber, and that if a tool is missing locally it is marked UNAVAILABLE rather than faking a successful compile. That is the behaviour to check on your own machine, because it determines whether a failed build is reported honestly.

Where Light Skills gets in the way

The honest limitation is that Light Skills is a prompt and process layer, not an execution engine. Nothing in the README claims the skills fetch papers, run your experiments or compile your PDF by themselves. They depend on the agent you attach them to and on the local toolchain. If MiKTeX is absent, light-typesetting marks the tool unavailable; the README does not say it will install it for you. If R is absent, the README states the figure skill should first ask whether to continue with an honest Python downgrade or to install and configure R. That is a reasonable fallback, but it means figure output can change language mid-project, which matters for reproducibility unless you record the choice.

The README is also silent on several things a team would want before adopting it. There is no documented rollback or uninstall procedure for the bootstrap script, no description of what --mode auto chooses between, and no stated behaviour for the --force flag beyond its presence in the example. The _shared/ directory's contents are not described in the README excerpt. There is no documented versioning policy tying skills to releases.

Finally, the name is a liability. The repository is a Chinese-language-first research skill pack, but the search results around "Light skills" point at Persona skill lists, Roblox games, and Lightcast's skills taxonomy. None of those are this project. A reader arriving from those searches will find nothing relevant here, and a reader looking for this project may land on a game wiki instead. The README has an English version at README.en.md, which helps, but the primary README and all documented prompts are in Simplified Chinese.

Compared with a general-purpose agent workflow or a reference manager

The closest alternative is not another skill pack but the default workflow: give a general agent your repository and a task, and let it decide. The difference in approach is that Light Skills pre-commits to a sequence. light-orchestrator selects a chain of skills and sets confirmation points, light-citation runs a four-gate check on references, light-consistency compares terminology and numbers across paper, figures, slides, code and supplementary material, and light-memory-pm keeps a ledger of unverified claims. A general agent has no equivalent of the ledger, so claims that were never checked tend to disappear into the final draft rather than being listed as open.

A reference manager such as Zotero solves a different problem: it stores and formats citations you already have. light-citation, as described, checks whether a citation is real, whether its DOI and link resolve, and whether the claim it is attached to is actually supported. Those are complementary, not competing. If your bottleneck is collecting PDFs, a reference manager is the right tool and Light Skills will not help. If your bottleneck is that your draft cites things nobody verified, light-citation is aimed at exactly that.

A third comparison is a hosted research assistant with a private knowledge base. The README states the public version requires no MCP and no local database, checking current information at task time. That is a deliberate trade: less setup and no private index to maintain, but also no persistent corpus, so every session re-checks from scratch. For a long project the ledger in light-memory-pm is the only continuity, and it lives in your repository, not in a service.

Maintenance, upgrade cost and the MIT licence

The repository is not archived, and the last push was on 2026-07-06, the same day as the v1.0.0 release. That is roughly two and a half months before today, so the project is not abandoned, but the release history shows a single tagged version, which means there is no track record of how upgrades are handled between releases. Plan for that: the bootstrap script copies files with --force, so re-running it after a pull overwrites the installed skills in your agent directory. If you or your team have edited any SKILL.md locally, those edits will be lost unless you keep them outside the skills tree or track them in version control.

The dependency surface is small and mostly optional. Git and Python 3.10+ are the base requirements. LaTeX and R are only needed for typesetting and for R-based figures, and the README treats missing tools as a marked unavailability rather than a hard failure. That keeps the install cheap, but it also means the quality of your output depends on which optional toolchains you actually installed.

The licence is MIT, which permits commercial and academic use, modification and redistribution provided the copyright notice and permission notice are retained. The repository ships LICENSE, CONTRIBUTING.md and SECURITY.md at the top level. This is a description of the licence identifier, not legal advice; if you are folding the skills into a product or a funded deliverable, read LICENSE yourself and confirm how the copyright notice should be carried.

Editorial conclusion

Adopt Light Skills if you are running a research, competition or innovation project with an AI coding agent and you want the agent to read files first, mark unknown facts as unknown, and generate figures through Python or R rather than by hand. Skip it if you want a hosted service, a skills taxonomy dataset, or anything unrelated to the name: the searches for Lightcast, Persona and Roblox have nothing to do with this repository. Before relying on it, run the check-only bootstrap command to confirm which skills landed where, and decide in advance whether a missing LaTeX or R toolchain should stop the workflow or trigger the documented fallback.

Frequently asked questions

Official sources

1. License: MIT
2. Light0305/Light-skills on GitHub
3. Project website
4. README
5. Releases