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.
git clone --depth 1 https://github.com/wbind-core/termpipe-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/agents/wbind-core/termpipe-mcp/memory-curator)<a href="https://agentmods.dev/agents/wbind-core/termpipe-mcp/memory-curator"><img src="https://agentmods.dev/badge/agents/wbind-core/termpipe-mcp/memory-curator/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/agents/wbind-core/termpipe-mcp/memory-curator"><img src="https://agentmods.dev/badge/agents/wbind-core/termpipe-mcp/memory-curator.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 | $0.01256 |
| Opus 5.5 | $0.00018 | $0.00502 |
| Sonnet 5.5 | $0.00009 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00126 |
Grade A, and why
memory-curator 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 126 lines · 45 tokens per session scan A ab907004d650
memory-curator is an agent published in the GitHub repository wbind-core/termpipe-mcp (0 stars, last pushed yesterday), with no licence file. It adds 45 tokens to every session and 1,256 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 2026-11-03.
Other agents, from other repositories
librarian
Use this agent for vault organization and maintenance — auditing folder structure, cleaning up frontmatter, finding orphan notes, and suggesting reorganization.
curate-chatlog
Curate the m3-chatlog store — clean, dedupe, decay ephemeral turns, prune abandoned conversations, promote high-signal chunks to long-term memory. Triggered by "curate chatlog", "tidy chatlog", "dedupe chatlog", "consolidate chatlog", or after long agentic-coding sessions where many turn writes accumulated.
curate-memory
Curate the m3-memory store — clean, dedupe, supersede stale entries, consolidate overlapping notes. Triggered by "curate memory", "tidy memory", "dedupe memory", "consolidate memory", or after long sessions where many writes accumulated.
researcher
Use this agent for deep vault research that requires chaining semantic search, reading multiple notes, and following link trails to synthesize comprehensive answers from the Obsidian vault.
Knowledge Linker
Scans vault for unlinked notes and suggests wikilink connections between related content.
im-local-db_knowledge-extractor
Expert in iterative knowledge extraction from extremely long chat logs. It processes contexts.md in chunks (approx. 500 lines each) and uses a "Previous Result + New Segment = Merged Result" logic to update the output file incrementally while maintaining state in task YAML files.