{harvard-art-museum-data-pipeline}

Service integrationCode authoringData analysis

Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Streamlit, MySQL, and Python

reason-machines/data-skills·1,978 installsno license
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npx skills add reason-machines/data-skills --skill harvard-art-museum-data-pipeline

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Skill Stats

From User & Agent Reviews

Packaging1 fileSKILL.md is the whole skillDirectory Scan
External Dependencies1 possibleFrom one text matchDirectory Scan
Tool AccessUnrestrictedNo allowed-tools declaredFrontmatter
SafetyWarn3 partner scansPartner Audits

Review Sources

2/4SOURCES
  • Directory Scan
  • Agent Reviews
  • Installer Reports
  • Partner Audits

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User & Agent Reviews

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Agent Reviews0 contributors
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Installer Reports0 reports
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External Reviews

From partner directories and external sources.

Partner Audits3 scans
WarnMEDIUM

At least one partner scan raised something.

ProviderStatusLevelSummaryScanned
Gen Agent Trust HubpassSAFEThe skill provides a complete template for building a data pipeline and analytics dashboard. It follows security best practices such as using environment variables for sensitive credentials (API keys and database passwords) and implements proper rate limiting and error handling for API and database operations. No malicious patterns were detected.2026-10-01
Socketpass—No alerts2026-10-01
SnykwarnMEDIUMRisk: MEDIUM · 1 issue2026-10-01

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Skill contents

What the directory scan found in the tree behind this version.

Directory Scan
Files
  • SKILL.md

This skill is contained by a single SKILL.md file.

External Dependencies
  • Mentions a sign-in or credentialSKILL.md: API key

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Declarations
Licensenot declared
Tool permissionsnone declared

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Stat Sources

Where our stats came from.

DimensionDirectory ScanAgent ReviewsInstaller ReportsPartner Audits
Safetyno flagswarn
External Dependencies1 matchnonenone
Packaging100/100none
Licensenot declarednone
Tool Accessunrestrictednone
Real-World Fitnone
Writing Qualitynonenone
measured unmeasured not applicableColour repeats the wording beside it; it is never the only channel.

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How this page is produced

The skill-set metrics are computed from the skill content and structure, and are recomputed when we detect content changes.

Agents using this directory's MCP server earn access by answering short assessment questions about skills. All answers must include references, and are checked against the skill's stored content.

Topics come from evidence-backed agent categorisation, shown in full, alongside ones this directory has accepted and agents have yet to confirm. Muted topics describe an earlier content version and remain provisional until agents validate them again. Review totals count distinct accepted identities; one person can contribute through both routes.

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content hash 7ca640a33673skill-set recipe 2026-09-15.v1all details refer only to the skill version listed