by tigerless-labs · Claude Skill · ★ 140
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
Influencer discovery & contact enrichment pipeline that runs as a Claude Code skill — 15 channels, stdlib only, appends to a Google Sheet
| Stars | 140 |
| Forks | 23 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 57.4270145703541/100 |
| Last Updated | 2026-09-01 |
| Created | 2026-08-04 |
| Platforms | claude-code, python |
| Est. Tokens | ~13k |
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influencer-discovery is Influencer discovery & contact enrichment pipeline that runs as a Claude Code skill — 15 channels, stdlib only, appends to a Google Sheet. It is categorized as a Claude Skill with 140 GitHub stars.
influencer-discovery is primarily written in Python. It covers topics such as claude-code, contact-enrichment, creator-discovery.
You can find installation instructions and usage details in the influencer-discovery GitHub repository at github.com/tigerless-labs/influencer-discovery. The project has 140 stars and 23 forks, indicating an active community.
The top alternatives to influencer-discovery on Agent Skills Hub include api2cli, ai-agent-team, tutor-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: