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 iPythoning/b2b-sdr-agent-template --skill supermemorygit clone --depth 1 https://github.com/iPythoning/b2b-sdr-agent-templateWrote 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/ipythoning/b2b-sdr-agent-template/supermemory)<a href="https://agentmods.dev/skills/ipythoning/b2b-sdr-agent-template/supermemory"><img src="https://agentmods.dev/badge/skills/ipythoning/b2b-sdr-agent-template/supermemory/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/ipythoning/b2b-sdr-agent-template/supermemory"><img src="https://agentmods.dev/badge/skills/ipythoning/b2b-sdr-agent-template/supermemory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.00417 |
| Opus 5.5 | $0.00000 | $0.00167 |
| Sonnet 5.5 | $0.00000 | $0.00083 |
| Haiku 4.5 | $0.00000 | $0.00042 |
Grade A, and why
supermemory 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 2mo 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
supermemory — AI Memory Engine
Semantic memory layer powered by vector search. Store, recall, and connect conversation insights across all customer interactions.
Architecture
Conversation → Extract Insights → Embed → Store (Vector DB)
↓
Query → Semantic Search → Relevant Memories → Inject into Context
Memory Types
| Type | TTL | Example |
|---|---|---|
| Customer Fact | Permanent | "Ahmed from Dubai, buys 50 units/quarter" |
| Conversation Insight | 90 days | "Interested in bulk pricing for Model X" |
| Market Signal | 30 days | "East Africa demand spike for product Y" |
| Effective Script | Permanent | "Opening with local market data → 3x reply rate" |
Commands
memory:add <text>— Manually add a memorymemory:search <query>— Semantic search across all memoriesmemory:list [type]— List recent memories by typememory:forget <id>— Delete a specific memorymemory:stats— Memory usage statistics
Auto-Capture
When enabled, the engine automatically extracts and stores:
- Customer preferences and requirements
- Price sensitivity signals
- Competitive mentions
- Purchase timeline indicators
- Relationship context (referrals, prior interactions)
Configuration
{
"provider": "lancedb",
"embedding_model": "{{embedding_model}}",
"auto_capture": true,
"capture_strategy": "last_turn",
"recall_top_k": 5,
"ttl_days": {
"customer_fact": null,
"conversation_insight": 90,
"market_signal": 30,
"effective_script": null
}
}
Integration
Works with:
- LanceDB (local, no external dependency)
- Supermemory Cloud (hosted, API key required)
- Memos (self-hosted note-taking)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 57 lines · 0 tokens per session scan A 210c8ac06d1b
supermemory is a skill published in the GitHub repository iPythoning/b2b-sdr-agent-template (191 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 417 tokens. 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.
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