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 commands/tm42/mnemograph/recallgit clone --depth 1 https://github.com/tm42/mnemographWhat 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 | $0.00010 | $0.00312 |
| Opus 5 | $0.00005 | $0.00156 |
| Sonnet 5 | $0.00002 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
recall 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 2d 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
Recall from Knowledge Graph
Use the mnemograph MCP server to retrieve relevant context from the knowledge graph.
Your Task
The user wants to recall information. Their query: $ARGUMENTS
Instructions
-
Determine recall depth based on the query:
- If the query is a quick orientation question (e.g., "what's in memory?", "overview") → use
depth: shallow - If the query is task-specific (e.g., "auth decisions", "API patterns") → use
depth: medium - If the query needs deep exploration (e.g., "everything about X", "full context") → use
depth: deep
- If the query is a quick orientation question (e.g., "what's in memory?", "overview") → use
-
Call the mnemograph recall tool with:
query: The user's query (use $ARGUMENTS)depth: As determined aboveformat: "prose" for human-readable output
-
Present the results clearly:
- Summarize the key entities and their relationships
- Highlight any decisions or learnings that are relevant
- If the query found nothing useful, suggest what topics might exist
Example Queries
/recall auth→ medium depth search for authentication-related knowledge/recall→ shallow overview of what's in the graph/recall everything about the API design→ deep exploration
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.
- 2d ago First seen · 36 lines · 10 tokens per session scan A 6d00db7b45d7
recall is a command published in the GitHub repository tm42/mnemograph (2 stars, last pushed 6mo ago), licensed MIT. It adds 10 tokens to every session and 312 once invoked, about $0.0001 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 2026-08-31.
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