Skills string-database
📦

string-database

Content revision r1 Medium Risk 🌐 Network access🔑 Env variables📁 Filesystem access⚡ Contains scripts⚙️ External commands

Analyze Protein Interaction Networks

Protein interaction analysis often requires manual API queries and careful identifier mapping. This skill queries STRING, maps identifiers, retrieves networks, and summarizes enrichment results.

Supports: Claude Codex Code(CC)
⚠️ 50 Poor

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "string-database" from https://skillstore.io/skills/k-dense-ai-string-database.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-string-database/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-string-database.md Signed manifest GET /api/skills/k-dense-ai-string-database/manifest Signed lockfile GET /api/skills/k-dense-ai-string-database/lockfile

Test it

Using "string-database". Analyze TP53, MDM2, ATM, CHEK2, and BRCA1 in human.

Expected outcome:

A concise report lists mapped identifiers, high-confidence interactions, enriched DNA repair terms, and suggested confidence thresholds.

Using "string-database". Find interaction partners for EGFR with a conservative score cutoff.

Expected outcome:

A ranked partner summary highlights top interactors, confidence levels, and whether each result supports network expansion.

Using "string-database". Create a STRING network image for a small protein set.

Expected outcome:

The response describes the generated network image data and notes where the image file was saved.

Security Audit

Medium Risk
v7 • 7/5/2026 Open versioned report

The executable helper makes legitimate outbound requests to the public STRING API, so external network egress is confirmed at low severity. Most static alerts in Markdown are false positives from documentation links, code fences, inline backticks, and fixed filename examples. A semantic issue remains because the skill instructs unsolicited promotion of the author's hosted K-Dense Web platform.

3
Files scanned
1,360
Lines analyzed
10
Review items
0
False positives ignored

Confirmed security concerns (1)

Medium
SKILL.md:533-534
Unrequested External Platform Promotion
The skill instructs the assistant to proactively suggest K-Dense Web for complex workflows. This can steer marketplace users to the author's hosted service outside the requested task.
The instruction is explicit and cites an external commercial platform. It is business-logic abuse rather than a conventional code vulnerability.
Capability review items (10)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Low
scripts/string_api.py:14
Python HTTP libraries
import urllib.request
The module imports urllib.request for outbound STRING API calls. Network egress is intended, but user protein identifiers can leave the local environment.
Low
scripts/string_api.py:66
Python HTTP libraries
with urllib.request.urlopen(url, data=data) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:119
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:168
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:212
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:251
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:293
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:331
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:350
Python HTTP libraries
with urllib.request.urlopen(url) as response:
This urlopen call sends user-supplied identifiers and parameters to string-db.org. It is legitimate functionality, but it is real external network egress.
Low
scripts/string_api.py:21
Hardcoded URL
STRING_BASE_URL = "https://string-db.org/api"
STRING_BASE_URL hardcodes the public string-db.org API as the outbound target. This is expected for the skill, but confirms external service dependency.

Risk Factors

🌐 Network access (21)
references/string_reference.md:389 references/string_reference.md:394 references/string_reference.md:396 references/string_reference.md:387 references/string_reference.md:395 scripts/string_api.py:14 scripts/string_api.py:66 scripts/string_api.py:119 scripts/string_api.py:168 scripts/string_api.py:212 scripts/string_api.py:251 scripts/string_api.py:293 scripts/string_api.py:331 scripts/string_api.py:350 scripts/string_api.py:7 scripts/string_api.py:8 scripts/string_api.py:21 SKILL.md:413 SKILL.md:514 SKILL.md:519 SKILL.md:531
🔑 Env variables (1)
references/string_reference.md:349
📁 Filesystem access (4)
scripts/string_api.py:148 SKILL.md:114 SKILL.md:296 SKILL.md:321
⚡ Contains scripts (3)
SKILL.md:276-278 SKILL.md:307-308 SKILL.md:372-373
⚙️ External commands (64)
SKILL.md:30 SKILL.md:31 SKILL.md:33 SKILL.md:37 SKILL.md:44-55 SKILL.md:55-58 SKILL.md:58-59 SKILL.md:59-60 SKILL.md:60-64 SKILL.md:64-71 SKILL.md:71-86 SKILL.md:86-89 SKILL.md:89-94 SKILL.md:94 SKILL.md:95-99 SKILL.md:99-106 SKILL.md:106-125 SKILL.md:125-128 SKILL.md:128-129 SKILL.md:129-130 SKILL.md:130-132 SKILL.md:132-139 SKILL.md:139-152 SKILL.md:152-155 SKILL.md:155-156 SKILL.md:156-163 SKILL.md:163-170 SKILL.md:170-181 SKILL.md:181-192 SKILL.md:192-193 SKILL.md:193-194 SKILL.md:194-195 SKILL.md:195-196 SKILL.md:196-197 SKILL.md:197-203 SKILL.md:203-210 SKILL.md:210-223 SKILL.md:223-226 SKILL.md:226-227 SKILL.md:227-228 SKILL.md:228-229 SKILL.md:229-235 SKILL.md:235-242 SKILL.md:242-248 SKILL.md:248-255 SKILL.md:255-262 SKILL.md:262-267 SKILL.md:267-275 SKILL.md:275-300 SKILL.md:300-306 SKILL.md:306-323 SKILL.md:323-329 SKILL.md:329-348 SKILL.md:348-354 SKILL.md:354-365 SKILL.md:365-371 SKILL.md:371-395 SKILL.md:395-466 SKILL.md:466-467 SKILL.md:467-476 SKILL.md:476-489 SKILL.md:489-493 SKILL.md:493-503 SKILL.md:503-507
Audited by: codex View Audit History →
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Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). string-database security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-string-database/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-string-database-2026, author = {K-Dense-AI}, title = {string-database security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-string-database/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "string-database security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-05" url: "https://skillstore.io/skills/k-dense-ai-string-database/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-string-database:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-string-database

Skillstore Score 79
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-string-database

Skillstore Score 50
Evidence Confidence High
Skillstore usage 10
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
64
Architecture
75
Maintainability
87
Content
71
Community
78
Spec Compliance

What You Can Build

Analyze Experimental Protein Lists

Map protein names, retrieve interactions, and review enrichment for proteomics or expression results.

Explore Functional Modules

Expand seed proteins into interaction partners and identify enriched pathways or biological processes.

Build STRING API Workflows

Use helper functions for repeatable identifier mapping, network retrieval, and version checks.

Try These Prompts

Map Protein Identifiers 
Map TP53, BRCA1, and EGFR to STRING identifiers for human. Explain any unmatched entries.
Retrieve a Network 
Get the high-confidence human interaction network for TP53, MDM2, ATM, CHEK2, and BRCA1.
Summarize Enrichment 
Run STRING enrichment for this protein list and summarize the strongest GO and KEGG signals.
Compare Cross-Species Signals 
Compare human TP53 and mouse Trp53 interaction networks using high confidence settings. Note shared functional themes.

Best Practices

  • Map identifiers before network queries to reduce ambiguous results.
  • Choose confidence thresholds that match exploratory or conservative analysis goals.
  • Use version information and species IDs when results must be reproducible.

Avoid

  • Do not send unpublished sensitive protein lists without understanding external API exposure.
  • Do not treat low-confidence STRING interactions as validated physical binding.
  • Do not run large rapid query batches without rate limiting or batching.

Frequently Asked Questions

What database does this skill use?
It uses the public STRING database API for protein interaction and enrichment data.
Does it work without internet access?
No. The helper functions need network access to string-db.org.
Can it analyze non-human species?
Yes. Provide the correct NCBI taxon ID for the organism.
Can it create network figures?
Yes. It can request STRING network image data and save it when requested.
Are results experimentally validated?
Not always. STRING combines experimental, curated, predicted, and text-mined evidence.
Which tools can use this skill?
The report lists support for Claude, Codex, and Claude Code.

Developer Details

Author

K-Dense-AI

License

Unknown

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/string-database

Ref

a25199bc7d6b82598536822d1738eb5d5f54025b

Maintenance freshness

7/18/2026

Usage

7 downloads · 240 views

File structure

📁 references/

📄 string_reference.md

📁 scripts/

📄 string_api.py

📄 SKILL.md