Dianel555Dianel555/DSkills/skills/literature-searcher/SKILL.md

literature-searcher

Skill65 starsChanged 2 months agoMITView on GitHub

Search CrossRef, OpenAlex, PubMed, Semantic Scholar, and optional Scopus; deduplicate results, download open-access PDFs by DOI, classify papers, monitor new results, and analyze coverage. Use when asked to search literature, monitor a topic, download an open-access paper, classify papers, or analyze literature gaps.

Reads credentials

In this repository18 other files it gives its agents.
- ace-toolSkill
- agent-wikiSkill
- capability-evolverSkill
- cc-agySkill
- cc-codexSkill
- codex-ccSkill
- context7Skill
- exaSkill
- github-trending-analyzerSkill
- grill-meSkill
- grok-searchSkill
- ledger-tasks-yyloSkill
- ontolySkill
- plan-ledger-tasks-yyloSkill
- sequential-thinkSkill
- serenaSkill
- timeSkill
- wiki-yyloSkill

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.

Common
Distinctive
Very specific

Very specific:
Run all commands from skills/literature-searcher.
line 13

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.

Files

SKILL.md

Literature Searcher

Run all commands from skills/literature-searcher. The package uses only the Python standard library.

Search

python -m literature_searcher.searcher search "ionogel flexible sensor" --limit 10
python -m literature_searcher.searcher search "solid electrolyte" \
  --platform openalex --platform semantic_scholar --year-from 2020 --year-to 2024

Without --platform, the command searches CrossRef, OpenAlex, PubMed, and Semantic Scholar. Scopus requires ELSEVIER_API_KEY.

Download Open-Access PDFs

python -m literature_searcher.searcher download --doi 10.1016/j.cej.2022.135593

Downloads query Unpaywall by default. Enable the optional scihub-cli fallback explicitly with --scihub-fallback auto.

Incremental Monitoring And Insight

New-Item -ItemType Directory -Force data | Out-Null
Copy-Item monitor_config.json.template data/monitor_config.json

Set queries in data/monitor_config.json, then run:

python -m literature_searcher.monitor --config data/monitor_config.json
python -m literature_searcher.insight --input data/monitor_results.json

Monitoring uses the HTTPS DeepLX endpoint in DEEPLX_ENDPOINT by default and does not require a DeepL API key. To use a local OpenAI-compatible model service, set LOCAL_LLM_BASE_URL and LOCAL_LLM_MODEL, then pass --translator local_llm. Translation failures are recorded in the result JSON and do not stop monitoring.

Python API

Capability	Import	Entry point
Multi-platform search	`literature_searcher.searcher`	`search_papers()`
Result deduplication	`literature_searcher.searcher`	`merge_and_deduplicate()`
PDF download	`literature_searcher.downloader`	`Downloader.download_by_doi()`
Paper classification	`literature_searcher.classifier`	`PaperClassifier.classify()`
Incremental monitoring	`literature_searcher.monitor`	`run_monitor()`
Coverage analysis	`literature_searcher.insight`	`analyze_coverage()`

PaperClassifier.classify() and batch_classify() accept both mappings and objects with paper attributes.

Configuration

The package reads these optional variables from the process environment: LITERATURE_EMAIL, OPENALEX_EMAIL, OPENALEX_API_KEY, SEMANTIC_SCHOLAR_API_KEY, ELSEVIER_API_KEY, DEEPLX_ENDPOINT, LOCAL_LLM_BASE_URL, and LOCAL_LLM_MODEL. Copy .env.example to .env in the skill directory to load values automatically. Variables already exported by the calling shell take precedence.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

It worked
Partly
It didn't

What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free