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-remembergit 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-remember)<a href="https://agentmods.dev/skills/ozankasikci/global-agent-memory/gam-remember"><img src="https://agentmods.dev/badge/skills/ozankasikci/global-agent-memory/gam-remember/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-remember"><img src="https://agentmods.dev/badge/skills/ozankasikci/global-agent-memory/gam-remember.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 | $0.00021 | $0.00219 |
| Opus 5.5 | $0.00008 | $0.00088 |
| Sonnet 5.5 | $0.00004 | $0.00044 |
| Haiku 4.5 | $0.00002 | $0.00022 |
Grade A, and why
gam-remember 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 Remember
Treat text supplied with this invocation as the knowledge the user explicitly wants remembered. If it is empty or ambiguous, ask what should be remembered.
- Never store credentials, tokens, secrets, raw prompts, or transient logs.
- Search for duplicates in the current project with
memory_searchbefore creating anything. - If an existing active memory already covers the knowledge, report it instead of creating a duplicate.
- Otherwise call
memory_rememberwith a uniquerequest_id, the currentworking_directory, the narrowest correct scope, and one allowed V1 type:project,decision,fact,solution,preference,convention,session_summary,entity, orreference. - Create a candidate only. Report its ID and Vault-relative path and tell the user it is awaiting review.
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 First seen · 17 lines · 21 tokens per session scan A 966ee28c74c6
gam-remember is a skill published in the GitHub repository ozankasikci/global-agent-memory (14 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 219 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.