Enact – A package manager for AI agent tools

Hacker News

I built a tool registry for AI agents. Problem: Agents need tools, but the current options are awkward. You either wire up tools manually in your codebase, or you spin up MCP servers that bundle more than you need. Neither is discoverable, shareable, or safe to run. Enact is a package manager for executable tools: enact search "web scraping" enact run enact/firecrawl --args '{"url": "https://example.com"}' Each tool is a SKILL.md manifest (typed input/output schemas) plus code in any language. Tools run in containers, so dependencies are declared and execution is isolated. Key differences from MCP: - Tools, not servers. Install one capability, not a bundle. - Registry with discovery. Agents can search for tools at runtime. - Containerized. Run untrusted tools safely. - Sigstore signing for trust. Live registry: https://enact.tools CLI: npm install -g enact-cli Source: https://github.com/EnactProtocol/enact Would love feedback on the manifest format and whether this solves a real problem for folks building agents.

Visit product View on Hacker NewsDec 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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