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 skills/amanasmuei/amem/dashboardnpx skills add amanasmuei/amem --skill dashboardgit clone --depth 1 https://github.com/amanasmuei/amemWrote 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/amanasmuei/amem/dashboard)<a href="https://agentmods.dev/skills/amanasmuei/amem/dashboard"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/dashboard.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00204 |
| Opus 5 | $0.00016 | $0.00102 |
| Sonnet 5 | $0.00007 | $0.00041 |
| Haiku 4.5 | $0.00003 | $0.00020 |
Grade A, and why
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 6d 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
/amem:dashboard — Open Web Dashboard
Launch the amem interactive web dashboard in the browser.
Instructions
-
Run via Bash:
amem-cli dashboard -
Tell the user the dashboard is opening at
http://localhost:3333 -
Mention key features:
- Memory list with search, type filter, and tier filter
- Interactive knowledge graph (drag, click to inspect)
- Inline actions: Promote to Core, Demote, Expire
- Export as JSON or Markdown
- Session summaries timeline
- Reminders with status badges
-
If port 3333 is in use, suggest:
amem-cli dashboard --port=8080
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.
- 6d ago First seen · 32 lines · 33 tokens per session scan A cf3827b77630
dashboard is a skill published in the GitHub repository amanasmuei/amem (2 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 204 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 skills, from other repositories
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
ijfw-memory-audit
Audit and clean project memory files. Trigger: 'memory audit', 'clean memory', 'memory health', /memory-audit.
ijfw-recall
Surface relevant project memory at session start or on demand. Trigger: session start, 'recall', 'remember', 'what do you know', 'context', /recall.
ijfw-summarize
Generate optimized project context from codebase scan. Trigger: new project, no CLAUDE.md, /ijfw-summarize.
ijfw-handoff
Session handoff generation and loading. Trigger: session end, context full, /handoff.
repo-context-ledger
Maintain durable, evidence-based repository context whenever an agent initializes a repository, changes behavior, checkpoints or resumes work, switches AI tools or windows, collaborates through Git, prepares a pull request, or completes a coding task. Use the deterministic runtime to route bounded context, isolate…