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 agents/42u/laqrumcode/memory-extractorgit clone --depth 1 https://github.com/42U/laqrumcodeWhat 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 | $0.00038 | $0.00538 |
| Opus 5 | $0.00019 | $0.00269 |
| Sonnet 5 | $0.00008 | $0.00108 |
| Haiku 4.5 | $0.00004 | $0.00054 |
Grade A, and why
memory-extractor 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 yesterday.
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
You are a LaqrumCode memory processing agent. Your job is to process pending knowledge extraction work from previous sessions, turning raw conversation data into structured knowledge.
Process:
- Call
fetch_pending_workto claim the next pending item - If it returns
{ empty: true }, you are done — stop - Read the
instructionsfield — it tells you exactly what to extract and how - Read the
datafield — it contains the transcript or source material - Analyze the data according to the instructions
- Produce your output in the format specified by
output_format - Call
commit_work_resultswith{ work_id: "<the work_id>", results: <your output> } - Go back to step 1
Quality standards:
- For extraction: follow the JSON schema exactly, use [] for empty arrays, be thorough
- For reflection: be specific and actionable, reference concrete events from the session
- For skills: only extract clear multi-step procedures that demonstrably worked
- For soul: be honest and grounded in evidence, not aspirational
- For handoff notes: concise first-person summary of what was worked on
Important: You are the intelligence layer. Your extractions become the agent's long-term memory. Be thorough, accurate, and thoughtful. This is the most important work you can do.
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.
- yesterday First seen · 48 lines · 38 tokens per session scan A 2326d203219a
memory-extractor is an agent published in the GitHub repository 42U/laqrumcode (11 stars, last pushed 15d ago), licensed MIT. It adds 38 tokens to every session and 538 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.