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 skills add Akakaui/visual-browser-agent --skill retention-cleanupgit clone --depth 1 https://github.com/Akakaui/visual-browser-agentWrote 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/skills/akakaui/visual-browser-agent/retention-cleanup)<a href="https://agentmods.dev/skills/akakaui/visual-browser-agent/retention-cleanup"><img src="https://agentmods.dev/badge/skills/akakaui/visual-browser-agent/retention-cleanup/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/skills/akakaui/visual-browser-agent/retention-cleanup"><img src="https://agentmods.dev/badge/skills/akakaui/visual-browser-agent/retention-cleanup.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 | $0.00020 | $0.00650 |
| Opus 5 | $0.00010 | $0.00325 |
| Sonnet 5 | $0.00004 | $0.00130 |
| Haiku 4.5 | $0.00002 | $0.00065 |
Grade A, and why
retention-cleanup 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 8d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retention Cleanup Skill
When to Use
Use this skill for artifact lifecycle management:
- Clean up expired screenshots/recordings/reports
- Enforce per-run size limits
- Free disk space
- Compliance with data retention policies
Instructions
Automatic Cleanup
Configured via retention config:
rawVideoDays: 3- Delete recordings older than 3 daysscreenshotsDays: 14- Delete screenshots older than 14 daysreportsDays: 90- Delete reports older than 90 daysmaxRunSizeMb: 500- Max size per rundeleteExpiredAutomatically: true- Run on schedule
Manual Cleanup
- Call
delete_artifacts(paths[], confirm: true)for specific files - Or use retention manager directly for bulk operations
Run Size Enforcement
When adding artifacts to a run:
- Check
getRunSize(runId) - If >
maxRunSizeMb, delete oldest artifacts in that run until under limit
Retention Policies by Type
| Type | Default Retention | Use Case |
|---|---|---|
| Recordings | 3 days | Large, transient, for debugging |
| Screenshots | 14 days | Medium, reference for reports |
| Reports | 90 days | Small, long-term reference |
Safety
- Never delete outside
approvedDirectories - Always require
confirm: truefor manual deletion - Log all deletions with timestamp, reason, file list
- Respect
uploadArtifactsByDefault: false- don't auto-upload
Commands
# Automatic (runs on init and periodically)
retentionManager.cleanup()
# Manual specific files
delete_artifacts(["runs/screenshots/old.png", "runs/recordings/old.webm"], true)
# Check run size
retentionManager.getRunSize("run-123")
# Enforce limit
retentionManager.enforceRunSizeLimit("run-123")
Integration
- Called automatically after each run completes
- Hooked into
capture_screenshotandrecord_interactionvia event listeners - Reports deleted artifacts in run summary
Examples
User: "Clean up old recordings from last week"
Agent:
1. retentionManager.cleanup() → returns count deleted
2. Report: "Deleted 47 recordings older than 3 days"
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.
- 8d ago First seen · 92 lines · 20 tokens per session scan A 7f0baf91be4b
retention-cleanup is a skill published in the GitHub repository Akakaui/visual-browser-agent (0 stars, last pushed 14d ago), licensed MIT. It adds 20 tokens to every session and 650 once invoked, about $0.0001 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.
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