Github Tech Intelligence

GitHub trending tells you what's popular. This tells you what's worth your time — and why.

GitHub's built-in trending page has no API, no scoring, no history, and no way to automate anything. This actor adds the intelligence layer on top:

• Health score — is this library actively maintained or quietly dying?
• License risk — is this MIT-safe or will legal block the adoption?
• Viral potential score — is this gaining momentum or just a one-day spike?
• Week-over-week delta — how many stars did it gain since your last run?
• Hacker News cross-reference — is the developer community excited about it?
• Structured JSON output — pipe results into Slack, Notion, Airtable, or your own dashboard via Apify webhooks

Run once for a snapshot. Run on a schedule for ongoing intelligence.

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Who is this for?

You are...	You use this to...
VC / Investor	Spot the next breakout tool before TechCrunch writes about it — get a structured weekly brief instead of browsing manually
CTO / Engineer	Decide if a library is safe to adopt — health score, license risk, and contributor count in one record
Dev Tool Startup	Track how fast competitors are growing week over week — star delta and rank change on every run
Recruiter / Talent Firm	See what tech stack a company is actually building with, not just what they claim on their website
Product Manager	Monitor the developer ecosystem in your category — know what's gaining traction before your competitors do

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Modes

Trending — Find breakout repos by ecosystem

Pick one or more ecosystems from the dropdown. The actor searches all relevant GitHub topics automatically and surfaces the repos gaining the most traction.

Example input:

{
  "mode": "trending",
  "ecosystems": ["ai-llm", "java-spring"],
  "trendingDays": 30,
  "minStars": 100
}

Example output (per repo):

{
  "full_name": "spring-projects/spring-ai",
  "stars": 4200,
  "daily_star_velocity": 85.4,
  "signal": "🔥 Breakout",
  "viral_potential_score": 91,
  "viral_label": "🚨 Breakout Imminent",
  "viral_reasons": [
    "Gaining 85 stars/day — very high velocity",
    "Hot category: spring-ai — high VC/media attention right now",
    "Fork ratio 18% — strong real-world usage signal"
  ],
  "health_score": 84,
  "health_label": "Thriving",
  "license_spdx": "Apache-2.0",
  "license_risk": "safe",
  "license_risk_label": "✅ Safe — permissive, enterprise-friendly",
  "hn_post_found": true,
  "hn_points": 312,
  "hn_signal": "🟠 Hot — 312 points, 5d ago",
  "hn_url": "https://news.ycombinator.com/item?id=...",
  "ecosystem": "AI / LLM + Java / Spring"
}

---

Repo Analysis — Deep dive on specific repos

Full health and risk report on one or more repos. Use this before your team adopts a library or makes a build-vs-buy decision.

Example input:

{
  "mode": "repo_analysis",
  "repos": ["supabase/supabase", "qdrant/qdrant", "spring-projects/spring-boot"],
  "includeFounderIntelligence": true
}

Health score (0–100):

Signal	What it measures
Commit activity	How often code is being pushed in the last 30 days
Issue management	Open issues vs star count — high ratio = maintenance backlog
Community size	Number of unique contributors
Release cadence	How recently a new version shipped
Maturity signals	Stars, forks, fork-to-star ratio

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Org Intelligence — Map a company's GitHub presence

See what a company is publicly building, what languages dominate their stack, and which repos are their flagships.

Example input:

{
  "mode": "org_intelligence",
  "orgs": ["stripe", "vercel", "openai"]
}

Example output (summary record):

{
  "org": "stripe",
  "total_public_repos": 47,
  "total_stars": 38200,
  "top_languages": [
    { "language": "Ruby", "repo_count": 18 },
    { "language": "Go", "repo_count": 12 }
  ],
  "flagship_repos": [
    { "full_name": "stripe/stripe-js", "stars": 3800 }
  ]
}

---

Competitor Track — Monitor your competitive space

Watches specific repos and discovers new ones in the same topic area. Run on a schedule for weekly competitive snapshots.

Example input:

{
  "mode": "competitor_track",
  "ecosystems": ["databases"],
  "repos": ["qdrant/qdrant", "chroma-core/chroma"],
  "trendingDays": 7
}

At the end of every run, a single Intelligence Brief record is pushed summarising the top findings:

{
  "_type": "intelligence_brief",
  "headline": "🚨 2 breakout imminent | 🔥 4 high potential",
  "breakout_imminent": [...],
  "high_potential": [...],
  "worth_watching": [...],
  "top_pick": { "full_name": "...", "viral_potential_score": 94 }
}

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All Inputs

Field	Type	Default	Description
mode	select	trending	Which analysis to run
ecosystems	multi-select	["ai-llm"]	Pick one or more ecosystems — actor expands to all relevant topics automatically
topics	list	[]	Optional: add specific GitHub topic tags not covered by the ecosystem list
repos	list	[]	Specific owner/repo pairs (used in repo_analysis and competitor_track)
orgs	list	[]	GitHub org names to map (used in org_intelligence)
trendingDays	number	30	Look-back window in days (7–90)
minStars	number	100	Minimum stars to include a repo
maxResults	number	50	Total repos to return in your dataset (top N by viral score, after dedup)
maxResultsPerTopic	number	20	How many repos to fetch per GitHub topic tag (controls API cost and coverage breadth)
enableDeltaTracking	checkbox	false	Track changes between runs — adds stars_delta, rank_delta, viral_score_delta, is_new_this_run to every record. Most useful when running on a schedule.
deduplicateRepos	checkbox	true	When on, each repo appears once. Turn off to get one record per topic match — useful to see exactly which topics a repo is relevant to
organizationsOnly	checkbox	false	Exclude individual contributor repos — only show repos owned by companies or GitHub orgs
includeFounderIntelligence	checkbox	false	Fetch the GitHub profile of each repo owner — followers, company, credibility score
githubToken	secret	—	Free GitHub token — raises rate limit from 60 → 5,000 req/hr. Strongly recommended.

Ecosystems available

Ecosystem	Covers
AI / LLM	llm, agent, mcp, rag, generative-ai, vector-database, and more
Rust	rust, wasm, ebpf
Go / Infrastructure	go, golang, kubernetes, terraform, gitops
Java / Spring	spring-boot, spring-framework, spring-cloud, spring-ai, java, quarkus
Python	python, fastapi, django, pytorch, pandas
TypeScript / Frontend	typescript, nextjs, react, nodejs, vue
Mobile	flutter, react-native, kotlin, swift
DevOps / Cloud	docker, kubernetes, helm, ansible, ci-cd, observability
Databases	postgresql, duckdb, clickhouse, redis, mongodb
.NET / C#	dotnet, csharp, aspnetcore, blazor

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Output Fields (per repo)

Growth & Momentum

Field	Description
stars	Total star count
daily_star_velocity	Average stars gained per day in the trending window
stars_in_window	Stars gained during the trending window
signal	Viral Launch / Breakout / Surging / Growing / Steady / Established / Early Stage
viral_potential_score	0–100 score predicting breakout likelihood
viral_label	🚨 Breakout Imminent / 🔥 High Potential / 📈 Worth Watching / ➡️ Normal Growth
viral_reasons	Plain-English list of signals driving the score
topic_match_count	How many topic areas this repo appeared in — higher = broader ecosystem relevance
matched_topics	List of all topic tags this repo was found under
stars_delta	Stars gained since the previous run (only when enableDeltaTracking is on)
viral_score_delta	Change in viral potential score since the previous run
rank_delta	Rank change since last run — negative means moved up, positive means dropped
is_new_this_run	true if this repo wasn't in the previous run at all

Health

Field	Description
health_score	0–100 composite maintenance health score
health_label	Thriving / Healthy / Active / Slow / At Risk
commits_last_30d	Commits in the last 30 days
contributors_count	Number of unique contributors
open_issues_count	Current open issue count
last_release	Most recent release tag and date
is_archived	true if repo is abandoned/archived

License Risk

Field	Description
license_spdx	SPDX license identifier (e.g. MIT, Apache-2.0, AGPL-3.0)
license_risk	safe / weak_copyleft / restricted / viral / unknown
license_risk_label	✅ Safe / 🟡 Weak Copyleft / ⚠️ Restricted / 🚫 Viral Copyleft / ❓ No License
license_warning	Extra warning for licenses known to cause issues (BSL, SSPL)

Hacker News Signal

Field	Description
hn_post_found	Whether this repo was posted to HN
hn_points	Upvote score of the best matching HN post
hn_comments	Number of comments
hn_posted_days_ago	How many days ago it was posted
hn_signal	🔴 Viral / 🟠 Hot / 🟡 Trending / ⚪ Mentioned
hn_url	Direct link to the HN post

Founder Intelligence (optional)

Field	Description
founder_name	Display name of the repo owner
founder_company	Company listed on their GitHub profile
founder_followers	GitHub follower count
founder_account_age_years	How long they've been on GitHub
founder_twitter	Twitter/X handle if listed
founder_website	Personal or company website
founder_credibility_score	0–100 score based on public GitHub signals
founder_credibility_label	⭐ High / ✅ Solid / 🌱 Early / ❓ Unknown
founder_notable_employer	true if company matches known top-tier employers

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Cost

Item	Cost
GitHub API	Free — get a token at github.com/settings/tokens
Hacker News API	Free
Proxies	None needed
Apify compute	~$0.30–1 per run

A free GitHub token (no permissions needed) raises your limit to 5,000 requests/hour — enough for most runs. See SETUP.md for step-by-step instructions.

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Scheduling

Run on a schedule for ongoing intelligence:

Frequency	Best for
Daily	Competitor tracking in fast-moving spaces (AI, infra)
Weekly	Trending discovery across your ecosystem
Monthly	Org intelligence for a company watchlist

Set up schedules in the Apify console under Schedules. Enable Delta Tracking to automatically compare each run against the previous one — every repo will show how many stars it gained, whether its rank moved up or down, and whether it's newly appeared since your last run.

Recommended setup for ongoing monitoring:

1. Enable enableDeltaTracking
2. Set a schedule (weekly for most use cases, daily for fast-moving spaces like AI)
3. Connect the dataset to a webhook → push new records to Slack, Notion, or Airtable automatically