GitHub Trending Analyzer

A workflow protocol for tracking GitHub trending repositories with LLM-powered analysis. Fetches trending projects, enriches each with structured Chinese insights (what/analogy/help/who), classifies by themes, compares against historical snapshots, and generates dual-format reports (briefing + detailed).

Trigger Signals

• GitHub trending analysis
• Weekly tech trend report
• Repository discovery automation
• Incremental analysis refresh
• Theme-based repo categorization

Preconditions

• HTTP access to github.com/trending (no auth required for public trending)
• LLM backend capable of JSON-structured output (for the 4-field analysis schema)
• File system access for memory cache and report output
• HTML parsing capability (regex or DOM parser)

Strategy

Run the five-step pipeline in order.

Step 1: Fetch trending HTML

Construct the URL with time range and optional language filter:

https://github.com/trending[/{language}]?since={daily|weekly|monthly}

Fetch with a browser User-Agent to avoid bot detection. Parse the HTML to extract:

• name (org/repo)
• url (full GitHub link)
• desc (one-line description from the page)
• lang (primary language)
• stars (total stargazers count)
• today_stars (increment for this period)

Regex patterns (reference from source):

• Project name: <h2[^>]*>.*?<a href="/([^"]+)"
• Description: <p class="[^"]*col-9[^"]*"[^>]*>\s*(.*?)\s*</p>
• Language: <span itemprop="programmingLanguage">([^<]+)</span>
• Stars: parse from /stargazers link text after stripping HTML tags
• Today increment: ([\d,]+)\s*stars?\s*(?:this|today) (case-insensitive)

Step 2: Batch LLM analysis

For each batch of 5 projects (to avoid token limits), send this prompt to your LLM:

Analyze the following {N} GitHub Trending projects. Output strict JSON array.
Each project needs 4 fields:
- what: What it is (≤30 Chinese characters)
- analogy: Life analogy (one sentence)
- help: What it helps you do (2 items, each ≤40 chars, array)
- who: Who needs it (one sentence, ≤30 chars)

Project list:
1. org/repo (Language) — description...
2. ...

Output ONLY the JSON array, no other text. Example:
[{"name":"org/repo","what":"...","analogy":"...","help":["...","..."],"who":"..."}]

Parse the response:

1. Strip markdown code fences ( `json / ` )
2. Clean trailing commas: ,\s*([\]}]) → \1
3. Extract the JSON array via regex: \[.*\] (DOTALL)
4. Decode with json.loads() or equivalent
5. Match results back to projects by name suffix (case-insensitive)

Fallback: If array parsing fails, extract individual objects via bracket-counting and parse one by one.

Deep mode (optional): Use longer limits (what ≤50 chars, help 3 items) for richer analysis.

Step 3: Theme classification

Load the bundled theme_rules.json. For each project:

1. Concatenate name + " " + desc and lowercase
2. Iterate themes by priority order
3. Check if any keyword from the theme appears in the text
4. Assign to first matching theme
5. Default to "🌐 其他" if no match

Result: {theme_name: [projects...]} dictionary.

Step 4: Compute diff (optional)

If you maintain a memory cache (JSON file storing past runs):

[
  {
    "date": "2026-06-19",
    "since": "weekly",
    "lang": "python",
    "repos": [{"name":"...", "url":"...", "desc":"...", "lang":"...", "stars":..., "today_stars":..., "analysis":{...}}]
  }
]

Compare current repos against the latest entry with the same since value:

• new: projects in current but not in last
• hot: projects in both
• dropped: projects in last but not in current
• last_date: baseline timestamp

Step 5: Generate reports

#### Briefing (compact, for quick scan)

Structure:

# GitHub 热门趋势简报 - {date}

> 生成日期: {date}
> 来源: github.com/trending?since={since}
> 对比基准: {baseline_date}

## 🔥 新上榜
| 排名 | 项目 | 语言 | ⭐ Stars | 📈 今日新增 | 一句话说明 |

## ⭐ 持续热门
| 项目 | 语言 | ⭐ Stars | 📈 今日新增 | 为什么值得关注 |

## 📉 掉出榜单
| 项目 | 可能意味着什么 |

## 🎯 趋势主题
**{theme}** (N个): repo1(+stars), repo2(+stars), ...

## 💡 趋势解读
{LLM-generated trend insight based on all "what" summaries}

Trend insight prompt:

基于以下GitHub Trending项目摘要，用3-5句话分析当前最强技术趋势和驱动力：
{list of "name: what" for all projects}

#### Detailed (one section per project)

Structure:

# GitHub 详细分析 - {date}

## {Project Name}

**Stars**: {total} (+{increment})  
**Language**: {lang}  
**URL**: {github_url}

### 这是什么
{analysis.what}

### 生活化类比
{analysis.analogy}

### 它能帮你做什么
1. {analysis.help[0]}
2. {analysis.help[1]}

### 谁需要它
{analysis.who}

---

Save both to files with date-stamped names (e.g. trending_briefing_2026-06-19.md).

Constraints

Core rules

1. Batch size = 5 for LLM calls to avoid truncation. For 20 repos, make 4 separate calls.
2. JSON-only LLM output. The prompt explicitly forbids explanatory text. Parse defensively (strip fences, clean commas).
3. Name matching is fuzzy. Match by suffix (org/repo vs repo) and case-insensitive substring.
4. Theme priority matters. A project matching both "AI" and "Dev Tools" gets classified as "AI" (priority 1 < 4).
5. Memory is append-only list. Each run appends one entry. Keep last 30 to prevent unbounded growth.

Incremental modes (optional)

• Gap-fill mode: Load the latest memory entry → detect repos without analysis field → re-run LLM only for those → merge back → regenerate reports.
• Selective re-analysis: User specifies project names (comma-separated, partial match) → find matching repos in memory → re-run LLM with optional deep mode → update memory → regenerate reports.

Implementation hint: detect_gaps(repos) returns [r for r in repos if not r.get('analysis')].

Error handling

• HTML fetch fails: Retry once with 5s delay, then abort with clear error message.
• LLM returns non-JSON: Log warning, continue with raw description as fallback for that batch.
• Memory file missing: Treat as first run (no diff section in reports).

Output Protocol

Emit two Markdown files to a reports directory:

1. Briefing (trending_briefing_{date}.md): 4 sections (new/hot/dropped/themes) + trend insight.
2. Detailed (trending_detailed_{date}.md): One block per project with 4-field analysis.

Overwrite if file exists (same-day re-runs replace prior reports).

Console output during execution:

• "Fetching {since} trending..." → "Got {N} projects"
• "LLM batch {i}/{total}..." → "✅ Batch complete: {n} items"
• "📄 Briefing saved: {path}"
• "📄 Detailed saved: {path}"
• (Gap-fill) "Coverage: {covered}/{total} ({pct}%)"

Validation

Before emitting reports, confirm:

• All repos have name, url, desc, lang, stars, today_stars fields.
• At least one theme contains projects (not all "其他").
• LLM analysis covers ≥50% of projects (log warning if lower).
• Both report files are valid UTF-8 Markdown.
• Memory JSON is valid (can be reloaded without error).

Adapting and Extending

Custom themes

Edit the bundled theme_rules.json:

• Add new themes with emoji prefix and priority
• Extend keyword lists for existing themes
• Adjust priority order to prefer certain classifications

Alternative LLM schemas

The 4-field schema (what/analogy/help/who) is optimized for Chinese tech audiences. Adapt for other contexts:

• English reports: Change field names and prompt language
• Different insights: Replace "analogy" with "use cases" or "risks"
• Richer detail: Increase char limits in deep mode

Different trending sources

The HTML parsing patterns are GitHub-specific. To adapt for other platforms (Hacker News, Product Hunt):

• Replace Step 1 fetch logic
• Adjust regex patterns for that site's DOM structure
• Keep Steps 2-5 unchanged (LLM + themes + diff + reports)

Memory backends

The reference uses local JSON. For multi-agent or cloud deployments:

• Swap load_memory() / save_memory() with a DB or object storage client
• Maintain the same list-of-dicts schema
• Add concurrency locks if multiple agents run in parallel