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Tempuss AI Skills

GitHub ↗

Explorez les skills IA publiés de Tempuss et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

9

Étoiles des dépôts des skills indexés

45

Forks du dépôt

0

Moyenne des étoiles par skill indexé

5

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

9 skills

Explorer

GitHub

toss-patterns

Tempuss

Use when planning market strategy, learning from Toss's 7 success patterns (Pain Point, Trojan Horse, Friction Removal, Viral Loop, Data-Driven, Ecosystem, Regulation).

GitHub

template-generator

Tempuss

Generate standardized document templates (DOCUMENT, TECHNICAL, PROPOSAL, RESEARCH, SECURITY-QA, INDEX) with YAML frontmatter, Quick Reference sections, and consistent structure for professional documentation.

GitHub

thinking-framework

Tempuss

Use this when complex problem-solving, root cause analysis, strategic decision-making, or systematic thinking is needed. Applies 15 thinking methods with multi-agent orchestration and Clear-Thought MCP integrat...

GitHub

web-research

Tempuss

Use when researching markets, analyzing competitors, comparing technologies, or finding case studies. Ensures all findings include credible sources and URLs.

GitHub

doc-organizer

Tempuss

Apply Progressive Disclosure principles to organize large documentation projects. Restructure docs into hierarchical structure, reduce token usage by 95%+, and create README files for navigation.

GitHub

ai-persona-creator

Tempuss

Use when analyzing stakeholder psychology for negotiations, proposals, or persuasion. Creates research-backed personas revealing hidden motivations.

GitHub

market-strategy

Tempuss

Use when planning market entry, designing PMF strategy, or applying Toss's 16-question framework. Includes Pain Point Score, Trojan Horse path, 10x improvement guide.

GitHub

roi-analyzer

Tempuss

Use when preparing executive reports, evaluating investments, or calculating ROI/break-even/payback period. 30-minute analysis (87.5% time saving). Includes scenario analysis.

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.