Trending Repos Discovery

Discover and analyze trending GitHub repositories from TrendShift and other sources. Evaluate usefulness, extract learnings, and identify valuable skills/tools for adoption.

When to Use

• User asks "apa yang trending?" or "what's trending?"
• User wants to discover new tools/libraries
• User asks about specific trending repo
• Looking for inspiration or best practices
• Evaluating whether to adopt new tools

Sources

1. TrendShift (Primary)

URL: https://trendshift.io/

What it tracks:

• GitHub's most-discussed open-source projects
• Daily engagement rankings
• Real-time mentions from X/Twitter
• Discussions from Reddit and Hacker News

How to fetch: See references/trendshift-fetch-methods.md for TinyFish and GitHub API examples

2. GitHub Trending (Alternative)

URL: https://github.com/trending

Languages:

• All languages: https://github.com/trending
• Python: https://github.com/trending/python
• TypeScript: https://github.com/trending/typescript
• Rust: https://github.com/trending/rust

3. GitHub API (For Details)

See references/trendshift-fetch-methods.md for API usage examples.

Rate limit: 60 requests/hour (unauthenticated), 5000/hour (authenticated)

Analysis Workflow

Phase 1: Fetch Trending List

1. Fetch from TrendShift (primary source)
2. Extract repo names (owner/repo format)
3. Get first 10-20 repos (manageable batch)

Phase 2: Get Repo Details

For each repo, fetch from GitHub API:

• Stars, forks, watchers
• Description
• Language
• Topics
• Created/updated dates

Batch strategy:

• Fetch 10 repos at a time
• If rate limited, wait or use cached data

Phase 3: Categorize by Usefulness

For each repo, assess:

A. Relevance Score

Factor	Weight	Criteria
Stars	30%	> 10K = high, 1K-10K = medium, < 1K = low
Description match	30%	Keywords match user's interests
Language	20%	User's tech stack (Python, TypeScript, Rust)
Recency	10%	Updated in last 30 days
Topics	10%	Relevant tags (ai, agent, skills, tools)

B. Usefulness Categories

✅ Highly Useful:

• Directly applicable to user's projects
• Solves current problems
• Fills gaps in toolset
• High stars (> 10K) + active development

🟡 Potentially Useful:

• Interesting but not immediate need
• Requires evaluation/testing
• Medium stars (1K-10K)
• Niche use case

❌ Not Useful:

• Wrong tech stack (e.g., Mac-only for Linux user)
• Too niche (e.g., biology tools for software dev)
• Duplicate of existing tools
• Low stars (< 1K) + no clear value

Phase 4: Extract Learnings

For useful repos, identify:

1. Skills/Patterns

• What skills does it provide?
• What patterns does it use?
• What best practices does it demonstrate?

Example (obra/superpowers):

• Skills framework architecture
• Agentic workflow patterns
• Production-ready methodology

2. Tools/Libraries

• What tools does it introduce?
• What dependencies does it use?
• What integrations does it support?

Example (addyosmani/agent-skills):

• Production-grade engineering patterns
• Web performance optimization
• Chrome DevTools expertise

3. Concepts/Ideas

• What problems does it solve?
• What approaches does it take?
• What innovations does it introduce?

Example (andrej-karpathy-skills):

• LLM coding pitfalls
• AI code quality patterns
• Production AI best practices

Phase 5: Present Analysis

Format:

## 🔥 TOP TRENDING REPOS

### 1. owner/repo ⭐ X,XXX stars 🏆

**Description:** [One-line description]

**Berguna?** ✅ SANGAT / 🟡 MUNGKIN / ❌ TIDAK

**Why:**
- [Reason 1]
- [Reason 2]
- [Reason 3]

**Use Case:** [How user can use it]

**Action:** [What to do next]

---

## 🎯 RECOMMENDED ACTIONS

### Priority 1: MUST CHECK 🔥
1. [Repo 1] - [Why]
2. [Repo 2] - [Why]

### Priority 2: WORTH CHECKING ✅
3. [Repo 3] - [Why]

### Priority 3: MAYBE ⚠️
5. [Repo 5] - [Why]

## ❌ SKIP THESE
- [Repo X] - [Why not useful]

User Preference Patterns

For "Analisa trending repos"

When user asks to analyze trending repos:

DO:

• ✅ Fetch from TrendShift (most comprehensive)
• ✅ Get details for top 10-20 repos
• ✅ Categorize by usefulness (✅ 🟡 ❌)
• ✅ Explain WHY each is useful/not useful
• ✅ Provide actionable recommendations
• ✅ Group by priority (must check, worth checking, maybe, skip)

DON'T:

• ❌ Just list repo names (no value)
• ❌ Skip usefulness assessment
• ❌ Recommend everything (no filtering)
• ❌ Ignore user's tech stack/constraints

For "Ada yang berguna gak?"

When user asks if trending repos are useful:

Response pattern:

1. Quick verdict: "ADA YANG BERGUNA? ✅ BANYAK!" or "❌ TIDAK ADA"
2. Top picks: List 3-5 most useful repos
3. Why useful: Explain specific benefits
4. Action: What to do next (clone, study, adapt)

Example:

✅ ADA YANG MENARIK — review dulu sebelum klaim apapun

1. obra/superpowers — Skills framework
2. andrej-karpathy-skills — coding guidelines
3. mattpocock/skills — TypeScript / engineering workflows
4. addyosmani/agent-skills — frontend & performance practices

Mention star counts only when reporting raw findings; do not aggregate
them into "X K combined stars" or claim authors as contributors to a
downstream repo. Authors of upstream public skill repos have not endorsed
or contributed to any aggregator unless they explicitly say so.

MAU GUE CLONE & ANALISA SEKARANG? 🚀

Evaluation Criteria

For Skills Repos

Look for:

• ✅ SKILL.md or .claude directory (skills format)
• ✅ High stars (> 10K = trusted)
• ✅ Industry expert author (Karpathy, Pocock, Osmani)
• ✅ Production-tested (not toy examples)
• ✅ Applicable to user's work (coding, AI, web dev)

Red flags:

• ❌ Low stars (< 1K) + unknown author
• ❌ Toy examples only
• ❌ Outdated (not updated in 6+ months)
• ❌ Wrong tech stack (e.g., Java for Python user)

For Tools/Libraries

Look for:

• ✅ Solves real problem
• ✅ Active development (commits in last 30 days)
• ✅ Good documentation (README, examples)
• ✅ Compatible with user's stack
• ✅ Resource cost acceptable (< 500 MB disk, < 100 MB RAM for VPS users)

Red flags:

• ❌ Heavy resource usage (> 500 MB disk, > 100 MB RAM)
• ❌ Platform-specific (Mac-only for Linux user)
• ❌ Duplicate of existing tools
• ❌ Poor documentation

For Frameworks

Look for:

• ✅ Clear architecture
• ✅ Best practices demonstrated
• ✅ Production-ready
• ✅ Extensible/adaptable
• ✅ Good examples

Red flags:

• ❌ Over-engineered
• ❌ Too opinionated
• ❌ Hard to adapt
• ❌ No examples

Common Patterns

Skills Repos (High Value)

Pattern:

• Name: *-skills, skills, agent-skills
• Format: SKILL.md, .claude directory, or markdown files
• Author: Industry experts (Karpathy, Pocock, Osmani)
• Stars: > 10K
• Content: Production-tested patterns, best practices

Examples:

• obra/superpowers (186K stars) - Skills framework
• forrestchang/andrej-karpathy-skills (125K stars) - Coding quality
• mattpocock/skills (73K stars) - TypeScript mastery
• addyosmani/agent-skills (39K stars) - Performance

Action:

1. Clone repo
2. Study SKILL.md files
3. Identify useful patterns
4. Adapt to Hermes skills
5. Test in projects

Tool Repos (Medium Value)

Pattern:

• Name: Tool/library name
• Format: Python package, npm package, CLI tool
• Stars: 1K-10K
• Content: Specific functionality

Examples:

• antirez/ds4 (7.5K stars) - DeepSeek inference (Mac-only)
• huangserva/3DCellForge (1.5K stars) - 3D cell generation (niche)

Action:

1. Check resource cost (disk, RAM)
2. Check compatibility (OS, dependencies)
3. Evaluate usefulness (solves problem?)
4. If useful + lightweight → install
5. If heavy → find alternative

Framework Repos (High Value if Applicable)

Pattern:

• Name: Framework name
• Format: Full project structure
• Stars: > 10K
• Content: Architecture, patterns, examples

Examples:

• github/spec-kit (96K stars) - Spec-Driven Development

Action:

1. Study architecture
2. Understand patterns
3. Evaluate applicability
4. Adapt concepts (not full framework)
5. Document learnings

Pitfalls

1. Recommending Everything

Problem: User asks "ada yang berguna?", you list all 20 repos.

Solution:

• Filter by usefulness (✅ 🟡 ❌)
• Recommend top 3-5 only
• Group by priority
• Explain why each is useful

2. Ignoring Resource Constraints

Problem: Recommend heavy tool (> 500 MB) for VPS user.

Solution:

• ALWAYS check resource cost first
• If heavy → find lightweight alternative
• If no alternative → skip
• See vps-cleanup skill for thresholds

3. Not Explaining Why

Problem: List repos without explaining usefulness.

Solution:

• Always explain WHY useful
• Specific benefits (not generic "good tool")
• How user can use it
• What problem it solves

4. Recommending Wrong Tech Stack

Problem: Recommend Mac-only tool for Linux user.

Solution:

• Check compatibility (OS, dependencies)
• Check user's tech stack (Python, TypeScript, Rust)
• Skip incompatible tools
• Mention incompatibility in analysis

5. Not Providing Next Steps

Problem: User says "mantap" but doesn't know what to do.

Solution:

• Always end with action items
• "MAU GUE CLONE & ANALISA SEKARANG?" (Want me to clone & analyze now?)
• Specific commands (git clone, study, adapt)
• Clear next steps

Examples

See references/top-4-skills-repos-case-study.md for detailed example from session 2026-05-12.

References

• TrendShift: https://trendshift.io/
• GitHub Trending: https://github.com/trending
• GitHub API: https://docs.github.com/en/rest
• Fetch methods: references/trendshift-fetch-methods.md
• Case study: references/top-4-skills-repos-case-study.md