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 skills/mem0ai/mem0/mem0-context-loadernpx skills add mem0ai/mem0 --skill mem0-context-loadergit clone --depth 1 https://github.com/mem0ai/mem0What 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 | $0.00047 | $0.00581 |
| Opus 5 | $0.00023 | $0.00291 |
| Sonnet 5 | $0.00009 | $0.00116 |
| Haiku 4.5 | $0.00005 | $0.00058 |
Grade A, and why
mem0-context-loader 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.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Loader
Pre-fetches relevant memories to prime context before working on a task.
When to use
- Session start (invoke manually or auto-triggered by skill description matching)
- User starts work on a specific feature or file set
- Complex multi-step task begins
- User says "what do we know about X" or "context for X"
Steps
-
Extract topics from current message/task. Identify: file paths, module names, feature areas, error patterns.
-
Run 2-4 parallel
search_memoriescalls with different angles:Query angle Filter Purpose Feature/module name {"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "decision"}}]}Architecture decisions File paths mentioned {"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "convention"}}]}Coding patterns Error keywords (if any) {"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}, {"metadata": {"type": "anti_pattern"}}]}Known pitfalls Broad project context {"AND": [{"user_id": "<id>"}, {"app_id": "<pid>"}]}Catch-all -
Deduplicate results by memory ID across all search responses.
-
Output compact context block (max 10 memories):
context-loader: loaded <N> memories for "<task summary>"
- [decision] <content> [mem0:<short_id>]
- [convention] <content> [mem0:<short_id>]
- [anti_pattern] <content> [mem0:<short_id>]
- If zero results: output nothing. Don't announce empty context.
Constraints
- Read-only — never modify or delete memories
- Max 10 memories returned (most relevant only)
- Silent on empty — only surfaces findings if relevant context exists
- Skip memories already visible in current session context
Output formatting
IMPORTANT: Do NOT use markdown in your output. OpenCode TUI renders text verbatim — markdown like bold, ## headers, and | table | syntax appears as raw characters. Use plain text with indentation for structure. Use dashes for lists. Use spaces to align columns instead of markdown tables.
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 · 53 lines · 47 tokens per session scan A 6a9b93f29d1f
mem0-context-loader is a skill published in the GitHub repository mem0ai/mem0 (64,360 stars, last pushed 3d ago), licensed Apache-2.0. It adds 47 tokens to every session and 581 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.
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