Borrowing it
Nothing to install: this file belongs to Defend-AI-Tech-Inc/agent-discover-scanner. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Defend-AI-Tech-Inc/agent-discover-scanner/main/.claude/commands/grow-scanner.mdgit clone --depth 1 https://github.com/Defend-AI-Tech-Inc/agent-discover-scannerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/defend-ai-tech-inc/agent-discover-scanner/grow-scanner)<a href="https://agentmods.dev/commands/defend-ai-tech-inc/agent-discover-scanner/grow-scanner"><img src="https://agentmods.dev/badge/commands/defend-ai-tech-inc/agent-discover-scanner/grow-scanner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/defend-ai-tech-inc/agent-discover-scanner/grow-scanner"><img src="https://agentmods.dev/badge/commands/defend-ai-tech-inc/agent-discover-scanner/grow-scanner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00702 |
| Opus 5 | $0.00000 | $0.00351 |
| Sonnet 5 | $0.00000 | $0.00140 |
| Haiku 4.5 | $0.00000 | $0.00070 |
Grade A, and why
grow-scanner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grow AgentDiscover Scanner Traction
Autonomous task: increase real-world adoption of the AgentDiscover scanner.
Rules:
- NEVER fake metrics (no artificial stars, downloads, or issues)
- NEVER create fake user testimonials or reviews
- ONLY improve the product, documentation, and discoverability
- Every change must make the scanner genuinely more useful
Execute the following improvement categories in order:
1. First-Run Experience (highest impact)
- Audit the README.md: Can a security engineer go from zero to first scan in under 3 minutes?
- Ensure
pipx install agentdiscover && agent-discover-scanner scan-all ~/projects --duration 30works flawlessly - Add a "What You'll See" section with REAL example output (not mocked)
- Add a "Common Issues" section addressing known friction points
- Test the install path on a clean Python 3.10+ environment
2. Output Quality
- Ensure scan output is immediately actionable (not just raw data)
- Add a
--summaryflag that prints a human-readable executive summary - Add a
--reportflag that generates a markdown report suitable for sharing with management - Ensure AIBOM/CycloneDX output is valid and parseable by standard tools
- Add
--jsonoutput that's clean enough to pipe into jq
3. CI/CD Integration
- Create a GitHub Action:
defendai/agentdiscover-action - Usage: add to any repo's CI to scan for AI agents on every PR
- Output: SARIF format for GitHub Security tab integration
- Create the action.yml, Dockerfile, and documentation
- Write a blog-post-ready tutorial: "Add AI Agent Discovery to Your CI Pipeline in 5 Minutes"
4. Comparison Content
- Create docs/comparisons/ directory
- Write honest comparisons: AgentDiscover vs Cisco DefenseClaw Skills Scanner
- Write: AgentDiscover vs manual
grepfor AI frameworks - Write: AgentDiscover vs Nudge Security agent discovery
- Be honest about limitations — credibility > marketing
5. Integration Guides
- Create docs/integrations/ directory
- Write: "Using AgentDiscover with Splunk" (forward JSONL audit to Splunk HEC)
- Write: "Using AgentDiscover with Elastic/Kibana" (filebeat config for scan output)
- Write: "Using AgentDiscover in a Kubernetes cluster" (DaemonSet + Tetragon setup)
- Write: "Using AgentDiscover with GitHub Actions" (reference the action from #3)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 59 lines · 0 tokens per session scan A a2d14901d831
grow-scanner is a command published in the GitHub repository Defend-AI-Tech-Inc/agent-discover-scanner (21 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 702 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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