Research Skill repositories often promise the same outcomes: find papers, analyze data, write a manuscript, and review the result. Their operating models differ sharply. One may be a full workflow orchestrator, another a library of specialized procedures, and a third only a catalog and installer.
Installing all of them can create duplicate triggers, conflicting instructions, larger context, and dependencies nobody owns. A smaller rule works better: choose one primary Skill for the stage blocking your research, then add one verification, visualization, or writing Skill.
This guide reflects public repository documentation checked on September 8, 2026. Stars, branches, modules, and installation methods change; verify the current README and every registered SKILL.md before installation.
Pick by bottleneck, not by star count
| Current bottleneck | Start with | Why |
|---|---|---|
| Turning an idea into a research plan | Academic Research Skills | Connects research, drafting, review, and revision |
| Reading, citations, figures, and submission | Nature Skills | Modular research and publication tools |
| Biology, chemistry, drug discovery, scientific databases | Scientific Agent Skills | Deeper domain procedures and computing tools |
| AI and machine-learning experiments | AI Research SKILLs | Training, fine-tuning, evaluation, RAG, and MLOps |
| Experiments are done; the paper is weak | Research Paper Writing Skills | Narrow focus on argument and evidence alignment |
| Research depends heavily on code | Claude Scholar | Connects ideation, coding, experiments, and writing |
| Quantitative social science | Auto-Empirical Research Skills | Identification, estimation, and robustness workflows |
| Discovering compatible packages | AcademicForge | Catalog and installer rather than a research method |
| Retrieval and scientific prose | Claude Scientific Writer | Focused literature and writing workflow |
| Medicine and life sciences | Medical Research Skills | Domain evidence, protocols, statistics, and privacy |
For a general graduate workflow, compare Academic Research Skills with Nature Skills first. The former favors a continuous pipeline; the latter favors modules selected per task.
These projects belong to different layers
The ten repositories are useful to compare, but they are not ten interchangeable products.
| Type | Examples | What you receive | Common mistake |
|---|---|---|---|
| Workflow orchestrator | Academic Research Skills, Claude Scholar | Stage routing, state, review, and revision | Running two orchestrators with overlapping triggers |
| Specialist library | Nature Skills, Scientific Agent Skills | Independently callable domain procedures | Installing the entire library for one narrow task |
| Writing or verification tool | Research Paper Writing Skills, Claude Scientific Writer | A focused manuscript or citation workflow | Expecting it to design and run the experiment |
| Catalog and installer | AcademicForge | Discovery, filtering, and installation plans | Treating the catalog as the research engine |
The orchestrator decides what happens next. A specialist Skill defines how one stage runs. The model-access layer supplies the model, credentials, logs, and cost controls. Keeping those layers separate makes failures observable.
What the ten projects are built for
1. Academic Research Skills
ARS-Codex packages research, writing, reviewing, revision, and integrity checks for Codex. Use the Codex distribution instead of copying a Claude Code tree blindly.
2. Nature Skills
Nature Skills offers modular literature, writing, figure, citation, and specialist tools. Inspect dependencies before enabling a large bundle.
3. Scientific Agent Skills
Scientific Agent Skills covers procedural knowledge across genomics, cheminformatics, medical imaging, study design, and scientific communication. Its September 2026 paper describes 163 procedures in 16 areas.
4. AI Research SKILLs
AI Research SKILLs targets ML engineering: architectures, fine-tuning, post-training, distributed training, inference, evaluation, agents, and RAG.
5. Research Paper Writing Skills
Research Paper Writing Skills focuses on ML, CV, and NLP manuscripts, including section structure and claim-evidence checks.
6. Claude Scholar
Claude Scholar connects ideation, coding, experiments, writing, and publication. Its current documentation covers Claude Code, Codex CLI, Kimi Code CLI, and OpenCode.
7. Auto-Empirical Research Skills
Auto-Empirical Research Skills serves empirical research with identification, estimation, robustness, tables, and reproducibility checks. Start with one flagship workflow, not the entire catalog.
8. AcademicForge
AcademicForge is a site-first catalog and installer. Its retired branch and current installer architecture are not interchangeable.
9. Claude Scientific Writer
Claude Scientific Writer concentrates on retrieval, references, and scientific documents. Its primary packaging remains oriented toward Claude Code and CLI usage, so Codex users should evaluate adaptation requirements.
10. Medical Research Skills
Medical Research Skills covers evidence, protocol design, analysis, writing, and privacy for medical research. It does not replace clinical, statistical, or ethics review.
Three starter stacks you can use
Literature review and proposal
Use Academic Research Skills or Nature Skills as the primary workflow, then add a citation-verification or paper-search Skill. Require a query log, database list, search date, inclusion criteria, deduplication method, evidence matrix, and unresolved gaps.
AI and reproducible computation
Use AI Research SKILLs or Claude Scholar, then add code review or reproducibility checks. Preserve the environment lockfile, data version, training command, random seed, metric implementation, ablation table, and failed runs. A final chart without its execution trail is not reproducible evidence.
Medical and life-science research
Use a Medical Research Skills or Scientific Agent Skills module, then add evidence grading, privacy checks, and named human review gates. Separate generated suggestions from approval of clinical or research decisions.
Six checks before installation
- Identify whether the repository is upstream, an official platform distribution, or an unofficial adaptation.
- Read the license for research, teaching, and commercial-use restrictions.
- Locate every
SKILL.mdthat will become an active entry point. - Inventory Python, Node, Pandoc, LaTeX, database, and external API dependencies.
- Review scripts, network access, environment variables, and write locations.
- Record repository, branch, commit, install path, update method, and removal steps.
Use this installation review prompt:
Evaluate this research Skills repository: [GitHub URL]
Before installing:
1. Verify the upstream source, license, and maintained branch.
2. Read the README and every SKILL.md that will be registered.
3. List scripts, network calls, environment variables, and write locations.
4. Determine whether Codex supports it natively or needs a plugin, branch, or adapter.
5. Install only the smallest modules required for my current task.
6. Do not overwrite an existing Skill with the same name.
7. Run one minimal test with public, non-sensitive data.
Return the install path, invocation name, commit or version, test input, observed output, known limits, and uninstall method.
Evaluate this research Skills repository: [GitHub URL]
Before installing:
1. Verify the upstream source, license, and maintained branch.
2. Read the README and every SKILL.md that will be registered.
3. List scripts, network calls, environment variables, and write locations.
4. Determine whether Codex supports it natively or needs a plugin, branch, or adapter.
5. Install only the smallest modules required for my current task.
6. Do not overwrite an existing Skill with the same name.
7. Run one minimal test with public, non-sensitive data.
Return the install path, invocation name, commit or version, test input, observed output, known limits, and uninstall method.
A safer Codex installation sequence
- Read only: inspect repository structure, licenses, scripts, and every Skill entry point without running an installer.
- Reduce scope: select only the modules required for the current research stage.
- Record a snapshot: save repository URL, branch, commit, installed modules, dependencies, external services, and install path.
- Smoke-test with public data: do not use an unpublished manuscript, patient record, or partner dataset for the first run.
- Limit write access: begin with read-only project access and a dedicated output directory; allow source-file edits only after overwrite behavior is verified.
A minimal installation record should look like this:
repository: https://github.com/OWNER/REPO
branch: main
commit: COMMIT_SHA
installed_skills: [literature-review, citation-check]
install_path: ~/.codex/skills/
external_services: [OpenAlex, Crossref]
local_dependencies: [python>=3.11]
repository: https://github.com/OWNER/REPO
branch: main
commit: COMMIT_SHA
installed_skills: [literature-review, citation-check]
install_path: ~/.codex/skills/
external_services: [OpenAlex, Crossref]
local_dependencies: [python>=3.11]
A 30-minute acceptance test
Use one public paper, one precise question, and one answer you can verify. Check four behaviors:
- Routing: similar prompts consistently activate the intended Skill.
- Traceability: citations resolve to real DOI records or source passages.
- Reproducibility: commands, data versions, parameters, and random seeds are recorded.
- Safe failure: missing evidence produces a stop or uncertainty report, not a plausible invention.
A literature Skill should expose databases, queries, dates, screening decisions, and deduplication. A data-analysis Skill should preserve code, environment, inputs, outputs, and errors. A polished summary alone is not a research artifact.
Score competing Skills on the same public test case:
| Dimension | Weight | Pass condition |
|---|---|---|
| Task fit | 20 | Activates the correct workflow without irrelevant modules |
| Source traceability | 25 | DOI, title, URL, and supporting passage can be checked |
| Method completeness | 20 | Queries, data, parameters, exclusions, and limits are recorded |
| Reproducibility | 20 | A third party can rerun the workflow and recover the result |
| Safety and boundaries | 15 | Stops when evidence is missing and respects data and file permissions |
Treat this as an internal acceptance score, not a public ranking. Below 60 indicates an experimental assistant; 60–79 may support supervised work; a score above 80 still requires the researcher to own the final conclusion.
Three failure modes that polished output can hide
A real citation may not support the claim. Verify both the identity of the paper and the exact relationship between the cited passage and the sentence. Correlation, causation, animal evidence, and human outcomes cannot be exchanged silently.
Runnable code may remain irreproducible. Missing data versions, random seeds, dependency locks, preprocessing cells, and manual edits can change results. Preserve the entire path from source data to figure.
A complete workflow may cross a human decision gate. Research significance, paper exclusion, outlier handling, clinical interpretation, and ethics decisions need accountable human review. A useful Skill marks those gates explicitly.
Where ClaudeAPI/Apito fits
Skills specify procedure. Models perform interpretation and generation. ClaudeAPI/Apito provides model access, API keys, logs, and usage visibility. Manage these layers separately.
Teams can create dedicated keys for literature work, code analysis, and manuscript editing, then inspect model access and calls in the ClaudeAPI/Apito console. Use the current Base URL and model ID shown in the console instead of copying identifiers from an old article.
Separating keys by workflow also improves incident response. A literature key can be restricted to retrieval and summaries, computational work can have its own long-context budget, and manuscript editing can operate only on de-identified material. Logs can then be traced by key, time, model, and task without disabling the entire lab workflow.
De-identify sensitive data before model access. Human-subject data, clinical records, unpublished patents, and partner material may require ethics, contractual, security, or data-transfer review. A Skill cannot approve those decisions.
FAQ
Is the most-starred research Skill automatically the best?
No. Stars measure attention and are affected by age, promotion, and repository size. Task fit, maintenance, license, dependencies, and verifiable outputs matter more.
Should I install all ten projects?
Start with one primary Skill and one verification Skill. Expand only after a real workflow passes acceptance tests.
Can a Skill guarantee valid citations?
No. It can enforce retrieval and checking steps. You still need to verify DOI, title, authors, year, and whether the source supports the exact claim.
Are Claude Code and Codex Skills interchangeable?
Methods written in SKILL.md may transfer, while directories, manifests, scripts, permission models, and multi-agent behavior may not. Follow the current project documentation.
Final recommendation
Keep the Skill that leaves an inspectable trail of sources, commands, versions, decisions, and failed checks. Repository size and output speed are secondary.
ClaudeAPI/Apito is an independent third-party technical service. Claude, Claude Code, Anthropic, Codex, and the open-source project names belong to their respective owners. This article is based on public information and does not imply affiliation or endorsement.
