Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/alphaaiservice/cortex/docker-cleangit clone --depth 1 https://github.com/alphaaiservice/cortexWhat 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 | $0.00040 | $0.01177 |
| Opus 5 | $0.00020 | $0.00589 |
| Sonnet 5 | $0.00008 | $0.00235 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
docker-clean 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 yesterday.
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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docker Cleanup Automation
Clean up target: $ARGUMENTS (default: --all)
Step 1: Docker Environment Survey
First, assess the current Docker state:
echo "=== Docker Disk Usage ==="
docker system df
echo ""
echo "=== Running Containers ==="
docker ps --format "table {{.ID}}\t{{.Image}}\t{{.Status}}\t{{.Names}}"
echo ""
echo "=== All Containers (including stopped) ==="
docker ps -a --format "table {{.ID}}\t{{.Image}}\t{{.Status}}\t{{.Names}}"
echo ""
echo "=== Images ==="
docker images --format "table {{.Repository}}\t{{.Tag}}\t{{.Size}}\t{{.CreatedSince}}"
echo ""
echo "=== Dangling Images ==="
docker images -f "dangling=true" --format "table {{.ID}}\t{{.Size}}\t{{.CreatedSince}}"
echo ""
echo "=== Volumes ==="
docker volume ls
echo ""
echo "=== Networks (custom only) ==="
docker network ls --filter type=custom
Step 2: Generate Cleanup Report
Before cleaning, show what WILL be removed:
╔═══════════════════════════════════════════════╗
║ DOCKER CLEANUP PREVIEW ║
╠═══════════════════════════════════════════════╣
║ Stopped Containers: [count] ([size]) ║
║ Dangling Images: [count] ([size]) ║
║ Unused Images: [count] ([size]) ║
║ Unused Volumes: [count] ([size]) ║
║ Unused Networks: [count] ║
╠═══════════════════════════════════════════════╣
║ Total Reclaimable: [total size] ║
╚═══════════════════════════════════════════════╝
ASK FOR CONFIRMATION before proceeding with cleanup.
Step 3: Execute Cleanup
Based on the $ARGUMENTS flag:
--all (default) — Full cleanup
echo "=== Removing stopped containers ==="
docker container prune -f
echo ""
echo "=== Removing dangling images ==="
docker image prune -f
echo ""
echo "=== Removing unused images (not referenced by any container) ==="
docker image prune -a -f
echo ""
echo "=== Removing unused volumes ==="
docker volume prune -f
echo ""
echo "=== Removing unused networks ==="
docker network prune -f
echo ""
echo "=== Final disk usage ==="
docker system df
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.
- yesterday First seen · 174 lines · 40 tokens per session scan A fcf4fd7574d4
docker-clean is a command published in the GitHub repository alphaaiservice/cortex (1 stars, last pushed 25d ago), licensed MIT. It adds 40 tokens to every session and 1,177 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-31.
Other commands, from other repositories
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discuss
Refina una idea o feature antes de abrir un flujo completo de implementación.