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 cablate/memory-lancedb-mcp --skill lessongit clone --depth 1 https://github.com/cablate/memory-lancedb-mcpWrote 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/cablate/memory-lancedb-mcp/lesson)<a href="https://agentmods.dev/skills/cablate/memory-lancedb-mcp/lesson"><img src="https://agentmods.dev/badge/skills/cablate/memory-lancedb-mcp/lesson/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/cablate/memory-lancedb-mcp/lesson"><img src="https://agentmods.dev/badge/skills/cablate/memory-lancedb-mcp/lesson.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 | $0.00045 | $0.00278 |
| Opus 5.5 | $0.00018 | $0.00111 |
| Sonnet 5.5 | $0.00009 | $0.00056 |
| Haiku 4.5 | $0.00005 | $0.00028 |
Grade A, and why
lesson 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
Lesson Extraction & Storage
When triggered, extract and store lessons from the recent conversation context.
Steps
- Scan recent context — identify the pitfall, bug fix, or key insight just discussed
- Store technical layer (category: fact, importance ≥ 0.8):
Pitfall: [symptom]. Cause: [root cause]. Fix: [solution]. Prevention: [how to avoid]. - Store principle layer (category: decision, importance ≥ 0.85):
Decision principle ([tag]): [behavioral rule]. Trigger: [when]. Action: [what to do]. - Verify —
memory_recallwith anchor keywords to confirm both entries retrievable - Report — tell Master what was stored (brief summary)
Rules
- Keep entries short and atomic (< 500 chars each)
- If the lesson also affects a checklist or SKILL.md, update those files too
- If no clear lesson is found in recent context, ask Master what to store
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 · 29 lines · 45 tokens per session scan A e02c38922b43
lesson is a skill published in the GitHub repository cablate/memory-lancedb-mcp (0 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 278 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
memory-audit-belief-duel
A guided review process for conflicting beliefs or memories. It examines cases where two conclusions cannot both be true, including conflicts between a general rule and a more specific memory.
memory-audit-discoverability
A review guide for checking whether stored memories can be found at the right time. It focuses on where memories are attached, when they are triggered, whether aliases are missing, and whether a parent has too many children.
memory-audit-node-decomposition
A method for splitting an oversized knowledge note into smaller notes, each focused on one independent idea. It also explains how to keep useful core information in the original note.
memory-audit-pattern-extraction
A method for investigating repeated mistakes by comparing related memories and checking whether an earlier reminder failed. It looks at where the reminder was stored, when it was created, and whether it was strong enough to prevent the mistake.
memory-audit
An entry point for reviewing and maintaining an AI agent's stored memories. It describes how to remove repetition, preserve useful reasoning, and update memories when old conclusions no longer fit.
memory-audit-dead-data-purge
A review process for identifying memories that do not change future actions. It tests whether a note contains useful, experience-based guidance or only sounds meaningful without affecting decisions.