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 amanasmuei/amem --skill doctorgit 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/doctor)<a href="https://agentmods.dev/skills/amanasmuei/amem/doctor"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/doctor/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/amanasmuei/amem/doctor"><img src="https://agentmods.dev/badge/skills/amanasmuei/amem/doctor.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 | $0.00032 | $0.00256 |
| Opus 5 | $0.00016 | $0.00128 |
| Sonnet 5 | $0.00006 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
doctor 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 9d 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
Health Diagnostics
Run health diagnostics on the amem memory database.
Instructions
-
Run via Bash:
npx @aman_asmuei/amem doctor -
Present the results to the user, explaining any issues found.
-
Key metrics reported:
- Memories — total count
- Embeddings — percentage with semantic search enabled
- Core tier — token budget usage
- Graph edges — knowledge graph connections
- Stale — memories not accessed in 60+ days with low confidence
- Overdue reminders — any reminders past their due date
-
If issues are found, explain the suggestions:
- Low embedding coverage: restart MCP server or install
@huggingface/transformers - Core tier near budget: review core memories and demote less critical ones
- Stale memories: run
memory_consolidateto clean up - No corrections: suggest storing corrections when the AI makes mistakes
- Low embedding coverage: restart MCP server or install
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.
- 9d ago First seen · 32 lines · 32 tokens per session scan A 48e504093814
doctor is a skill published in the GitHub repository amanasmuei/amem (2 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 256 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-handoff
Session handoff generation and loading. Trigger: session end, context full, /handoff.
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.
repo-context-ledger
Record every behavior-changing feature addition, fix, and adjustment as durable, evidence-based repository knowledge, then use that ledger to continue accurately across AI windows, tools, Git collaboration, and pull requests. Use the deterministic runtime to route bounded context, isolate private drafts, publish…