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 ozankasikci/global-agent-memory --skill gam-dashboardgit clone --depth 1 https://github.com/ozankasikci/global-agent-memoryWrote 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/ozankasikci/global-agent-memory/gam-dashboard)<a href="https://agentmods.dev/skills/ozankasikci/global-agent-memory/gam-dashboard"><img src="https://agentmods.dev/badge/skills/ozankasikci/global-agent-memory/gam-dashboard/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/ozankasikci/global-agent-memory/gam-dashboard"><img src="https://agentmods.dev/badge/skills/ozankasikci/global-agent-memory/gam-dashboard.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 | $0.00019 | $0.00084 |
| Opus 5.5 | $0.00008 | $0.00034 |
| Sonnet 5.5 | $0.00004 | $0.00017 |
| Haiku 4.5 | $0.00002 | $0.00008 |
Grade A, and why
gam-dashboard 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 1mo 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.
What it actually says
GAM Dashboard
Call memory_dashboard_open with open_browser=true. The MCP runtime starts the
authenticated localhost dashboard server on demand when needed. Report success without
printing or inventing a dashboard URL.
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.
- 1mo ago Changed · +2 lines 825189ef9fca
- 1mo ago First seen · 11 lines · 19 tokens per session scan A 0637baffab3d
gam-dashboard is a skill published in the GitHub repository ozankasikci/global-agent-memory (14 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 84 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
memex-best-practices
Zettelkasten best practices for building a high-quality knowledge graph.
memex-agentic-memory
A-MEM-inspired agentic memory workflow for structured knowledge capture.
memex-organize
Periodic maintenance of the Zettelkasten card network.
memex-retro
Save insights from completed tasks to Zettelkasten memory.
memex-recall
Load prior knowledge from Zettelkasten memory when the task likely benefits from past context.
memex-sync
Sync Zettelkasten cards across devices via git.