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Vigil – Zero-dependency safety guardrails for AI agent tool calls

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Vigil – Zero-dependency safety guardrails for AI agent tool calls

We run 15 AI agents on a production server with full shell access. One of them tried to rm -rf a directory it shouldn't have touched. Another started curling cloud metadata endpoints. We wrote some hardcoded rules to catch the obvious stuff, then realized we were building the same safety layer everyone else will need too. So we extracted it into a library. Vigil is a deterministic rule engine that inspects AI agent tool calls before they execute. 22 rules across 8 threat categories: destructive shell commands, SSRF, path traversal, SQL injection, data exfiltration, prompt injection, encoded payloads, and credential exposure. It's not an LLM wrapper — we don't trust an LLM to guard another LLM. Pure pattern matching, zero dependencies, <2ms per check, works completely offline. npm install vigil-agent-safety import { checkAction } from 'vigil-agent-safety'; const result = checkAction({ agent: 'my-agent', tool: 'exec', params: { command: 'rm -rf /' }, }); // result.decision → "BLOCK" // result.reason → "Destructive command pattern" // result.latencyMs → 0.3 It plugs into MCP servers, LangChain tool chains, Express middleware, or anything else. MIT licensed, no API keys, no network calls, no telemetry. This is v0.1 — probably too aggressive for some use cases. Next up is a YAML policy engine (v0.2) and an MCP proxy. We'd love feedback on the rule set, false positive experiences, and threat categories we're missing. GitHub: https://github.com/hexitlabs/vigil

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
88%88% 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 HackersIH features products with proven revenue · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, 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
26%26% 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
22%22% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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