Go Modules Intelligence — Package Health & Risk (AI)

Before you add a Go dependency, know what you're pulling in. Give this Actor a list of Go module paths and it turns each one into a clear intelligence record: what it's licensed under, when it was last released, how popular its source repo is, whether it has known security advisories, how it scores on the OpenSSF Scorecard — and an AI risk read that tells you plainly whether a module is active, stale, or carrying supply-chain risk.

You bring the module paths. It hands back a dependency due-diligence sheet.

Go has no central download counter, so the headline value here is the security / supply-chain angle: OSV advisories and the OpenSSF Scorecard, not vanity download numbers.

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The flow

1. Paste Go module paths            2. The Actor gathers facts            3. Get an AI risk read
   (github.com/gorilla/mux,   →        deps.dev API (Google OSI):    →      advisories · scorecard ·
    golang.org/x/crypto, ...)          versions · advisories · stars        stars · license · maintenance

1. Paste Go module paths into the modules field — one per line.
2. The Actor pulls public data for each from the deps.dev API (Google's Open Source Insights): module versions and release dates, the latest version's licenses and OSV security advisories, and — via the linked source project — repository stars/forks/open issues and the OpenSSF Scorecard. No API key, no scraping of login-walled pages, no anti-bot.
3. The AI layer reads the facts and returns a maintenance signal, a popularity tier, a security posture, concrete risk flags and a two-sentence summary — grounded only in the data, never invented.

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What you get per module

For every module, the Actor returns:

1. Identity & release — latestVersion (the default/latest tag), versionCount, lastRelease (ISO timestamp), lastReleaseAgeDays (how long since the newest release), isDeprecated and deprecatedReason.
2. Security & supply chain — advisoryCount and advisoryKeys (the OSV / Go vulnerability database advisories affecting the latest version) and scorecardScore (the OpenSSF Scorecard overall score, 0–10).
3. Footprint & license — licenses (SPDX identifiers for the latest version) and the repository / homepage links.
4. Popularity signals — starsCount, forksCount and openIssuesCount from the linked source project (GitHub/GitLab-backed), so you can tell a ubiquitous library from a weekend experiment even without download numbers.

Then the AI layer (OpenAI gpt-4o-mini, a fast and low-cost model) adds:

• maintenanceSignal — active, maintained, stale, or abandoned, from release recency and deprecation.
• popularityTier — niche, growing, popular, or ubiquitous, from repository stars.
• securityPosture — strong, adequate, weak, or at-risk, from known advisories and the OpenSSF Scorecard.
• riskFlags — short, data-backed phrases: "known security advisories (N)", "unmaintained (last release >18mo)", "deprecated module", "no license declared", "low OpenSSF scorecard".
• aiSummary — two factual sentences on the module and its risk posture.
• suggestedAlternatives — only well-known, widely-trusted Go substitutes for the same job, else [].

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Three ways people use it

• Go dependency due diligence — before a new module lands in go.mod, run a Go module analysis to see its license, release recency, repository popularity and — crucially — whether it carries known security advisories. Catch a deprecated module, an unpatched CVE or a low-scorecard project before it's in your build.
• Supply-chain risk — audit an existing go.mod for Go dependency risk: surface modules with open OSV advisories, stale projects and weak OpenSSF Scorecards across your whole stack in one run. A standing input for your supply chain security reviews.
• Library selection — comparing candidates for the same job? Line them up and let stars, security posture, scorecard and suggestedAlternatives make the call obvious.

Keywords: Go module analysis, Go dependency risk, supply chain, OSV advisories, OpenSSF Scorecard.

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Input

Field	Type	Notes
modules	array	Required. Go module paths, one per line (e.g. github.com/gorilla/mux, golang.org/x/crypto). @version pins and https:// prefixes are tolerated and normalised.
maxResults	integer	Cap how many modules to analyze. Default 200.
maxConcurrency	integer	Modules processed in parallel. Default 6.
model	string	OpenAI model override. Default gpt-4o-mini.

Minimal input example

{
  "modules": ["github.com/gorilla/mux", "golang.org/x/crypto"]
}

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Output example

{
  "module": "github.com/gorilla/mux",
  "latestVersion": "v1.8.1",
  "isDeprecated": false,
  "deprecatedReason": null,
  "licenses": ["BSD-3-Clause"],
  "lastRelease": "2023-10-18T11:23:00Z",
  "lastReleaseAgeDays": 725,
  "versionCount": 380,
  "advisoryCount": 0,
  "advisoryKeys": [],
  "repository": "https://github.com/gorilla/mux",
  "homepage": "https://gorilla.github.io",
  "starsCount": 21841,
  "forksCount": 1892,
  "openIssuesCount": 46,
  "scorecardScore": 4.9,
  "maintenanceSignal": "stale",
  "popularityTier": "popular",
  "securityPosture": "adequate",
  "riskFlags": [],
  "suggestedAlternatives": ["github.com/go-chi/chi", "github.com/julienschmidt/httprouter"],
  "aiSummary": "gorilla/mux is a popular, BSD-licensed HTTP router with over 21k stars and no known security advisories against its latest release. Its last tagged release is more than a year old, which is common for mature, stable Go libraries."
}

A module that is not found on deps.dev comes back with the same shape plus an error field (and is not charged).

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Pay-per-event pricing

This Actor uses pay-per-event billing with a single event:

Event	Price	Charged when
module-analyzed	$0.10	Once per module successfully analyzed (facts gathered + AI risk read produced).

The Actor logs its OpenAI token usage and the estimated AI cost per module at the end of every run, so the event price always stays safely above the model cost.

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Data source

Everything comes from public, key-less sources: the deps.dev API (Google's Open Source Insights) — https://api.deps.dev/v3/systems/go/packages/<module> for versions and release dates, .../versions/<version> for licenses, OSV advisories and repository links, and https://api.deps.dev/v3/projects/<project> for stars, forks, open issues and the OpenSSF Scorecard. deps.dev returns clean JSON, covers Go first-class and has no anti-bot. A missing source project (so no stars/scorecard) is handled gracefully — the record still ships, just without those fields.

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Setup

The AI layer calls the OpenAI API. Set the environment variable / secret OPENAI_API_KEY on the Actor. The fact-gathering step (licenses, advisories, scorecard, popularity, maintenance) needs no key.

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Use it from an AI agent (Apify MCP server)

This Actor is available through the Apify MCP server, so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of modules and read back the intelligence records — with no custom integration code.

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Run it locally

npm install
# put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...
apify run -p

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Related actors

• npm Package Intelligence — The Node.js twin of this Actor: downloads, dependencies, maintenance and AI risk for any npm package.
• PyPI Package Intelligence — The Python twin: downloads, dependencies, maintenance and AI risk for any PyPI package.
• GitHub Repo Analyzer — Health, activity and maintenance signals for any GitHub repository.
• CVE Vulnerability Lookup — Look up known vulnerabilities and severity for packages and software.

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Part of the No Niner toolkit — no-code web scraping, enrichment & AI data tools, all pay-per-use. Browse the full set at noniner.io.