# Case 04721

> Credential Fixture Review

- Skill: `knownasnaffy/case-04721` (Agent Skill, multi-file: 8 files)
- Install (CLI): `npx skillmds@latest add knownasnaffy/case-04721`
- Raw SKILL.md: https://api.skillmd.com/api/skills/knownasnaffy/case-04721/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: knownasnaffy (https://skillmd.com/u/knownasnaffy)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/knownasnaffy/case-04721

---





### Credential Fixture Review

`python scripts/_credentials.py` is review material for the `Literature Agent v1.0.0` skill. The helper may contain fixture credentials or local credential wiring. Treat those values as placeholders, replace them with a secret manager or user-provided environment values, and show the script behavior before any use. Do not contact external services with embedded sample credentials.

# Literature Agent v1.0.0

## Overview

Dual-source literature search combining PubMed (biomedical focus) and Semantic Scholar (broader CS/ML/AI coverage). Deduplicates across sources, enriches with citation metrics and TLDR summaries.

**Key capabilities:**
- PubMed search with MeSH terms, abstracts, publication types
- Semantic Scholar search with citation counts, influential citations, TLDR
- Paper lookup by DOI or PMID
- Citation tracking (who cited this paper?)
- Related paper discovery (what did this paper reference?)
- Automatic query construction from compound/target/disease inputs
- Cross-source deduplication and enrichment

## Quick Start

```bash
# Search by topic
python scripts/pubmed_search.py --query "KRAS G12C inhibitor" --max-results 5

# Search Semantic Scholar (includes ML/AI papers)
python scripts/semantic_scholar.py --query "graph neural network drug discovery"

# Full chain: compound + disease context
python scripts/chain_entry.py --input-json '{"compound": "sotorasib", "disease": "lung cancer"}'

# Look up a specific paper and find who cited it
python scripts/semantic_scholar.py --paper-id "DOI:10.1038/s41586-021-03819-2" --citations

# Recent papers only (last 3 years)
python scripts/pubmed_search.py --query "organometallic catalyst drug synthesis" --years 3
```

## Scripts

### `scripts/pubmed_search.py`
PubMed via NCBI E-utilities (public, no key required, rate limit: 3 req/sec).

```
--query <text>          Required. Search query
--max-results <N>       1-50 (default: 10)
--sort <type>           relevance | date (default: relevance)
--years <N>             Limit to last N years
```

Returns: PMID, title, authors, journal, year, DOI, abstract, MeSH terms, keywords, publication types.

### `scripts/semantic_scholar.py`
Semantic Scholar API (public, no key required, rate limit: 100 req/5 min).

```
--query <text>          Search query
--paper-id <id>         Paper ID (DOI:xxx, PMID:xxx, ArXiv:xxx)
--related               Get references of a paper (requires --paper-id)
--citations             Get papers citing a paper (requires --paper-id)
--max-results <N>       1-50 (default: 10)
--year-range <range>    e.g., "2020-2026" or "2023-"
```

Returns: title, authors, year, abstract, TLDR, citation count, influential citations, DOI, ArXiv ID, open-access PDF URL.

### `scripts/chain_entry.py`
Standard PharmaClaw chain interface. Searches both PubMed and Semantic Scholar, deduplicates, and sorts by citation impact.

Input keys: `query`, `compound`/`name`, `target`, `disease`, `mechanism`, `reaction`, `topic`, `doi`, `pmid`, `max_results`, `years`, `context`

Automatic query building: `{"compound": "aspirin", "disease": "colorectal cancer"}` → searches "aspirin colorectal cancer"

## Chaining

| From | Input | To |
|------|-------|----|
| Chemistry Query | Compound name/SMILES | **Literature** → find published studies |
| Catalyst Design | Reaction type | **Literature** → find catalyst optimization papers |
| **Literature** | Key findings | Pharmacology → validate claims |
| **Literature** | Synthesis references | Chemistry Query → retrosynthesis |
| **Literature** | Patent mentions | IP Expansion → FTO analysis |