# 900+ Downloads, 1 Star, 1 Comment: What I Learned Launching a Security Tool

> Source: <https://dev.to/dockfixlabs/900-downloads-1-star-1-comment-what-i-learned-launching-a-security-tool-1pc5>
> Published: 2026-06-30 22:40:08+00:00

Two weeks ago I launched [AgentGuard](https://github.com/dockfixlabs/agentguard), an open-source static analysis tool for AI agent security. Here is what the data says.

**PyPI discoverability is real.** The `pip install`

flow means people find the package through PyPI search, not just GitHub. 920 downloads with 1 star means the conversion from "found it" to "starred it" is low, but the install-to-star ratio is normal for developer tools.

**Dev.to drives traffic.** The "Beyond Regex" technical deep-dive got 44 views -- the most of any article. Developers want technical depth, not marketing. The MCP security guide got a real comment from a peer building a complementary tool.

**OWASP ASI Top 10 is a strong positioning.** Nobody else covers all 10 categories in an open-source tool. That differentiation matters.

**Awesome-list PRs are slow.** Two PRs submitted, both mergeable, neither merged after a week. Maintainers of these lists have their own priorities. Not a failure, just patience required.

**Zero community contributions.** No PRs from external contributors. The good-first-issues are there (Go support, Java support) but nobody has picked them up. The project needs more visibility before contributors arrive.

**The 0-stars problem is self-reinforcing.** Developers check star counts before trying a tool. 1 star does not signal "trusted." This is the hardest loop to break for new open-source projects.

**Launch with a comparison table.** AgentGuard vs. Semgrep vs. CodeQL for AI agent security. Developers want to know "why not just use Semgrep?" before they install anything.

**Ship the GitHub Action on day one.** The `action.yml`

was added in v0.3.4 -- two weeks late. CI/CD integration is the #1 thing developers look for in a security tool.

**Write a "how to break an AI agent" post first.** Show the vulnerability, then the tool. The MCP security guide performed best because it led with the problem, not the product.

The biggest engineering mistake was **scanning my own code**. AgentGuard's regex rules match patterns like `eval\(`

and `os\.system`

-- which appear in the rule definitions themselves. First self-scan: 94 findings, 69 critical. All false positives.

The fix: skip the `rules/`

directory and test files by default. Add `--include-tests`

for explicit test scanning. Self-scan went from 94 to 2 (acceptable patterns in setup.py).

Lesson: **your security tool should be able to scan itself without screaming.** If it cannot, users will not trust it on their code either.

The next milestone is **AST-based taint tracking** (v0.4.0). Regex gets you 100% on a curated benchmark, but real codebases have patterns regex cannot see:

```
# Regex cannot track this
template = "Answer: {input}"
prompt = template.format(input=user_data)
```

This requires parsing the AST, tracking variable assignments, and following data flow from sources to sinks. Same approach as Semgrep and CodeQL, but specialized for LLM-specific sinks (`openai.chat.completions.create`

, `messages`

, `prompt`

).

If you want to follow along or contribute: [github.com/dockfixlabs/agentguard](https://github.com/dockfixlabs/agentguard)

*AgentGuard is MIT-licensed. Install with pip install dfx-agentguard.*